• A developer says Claude Code just deleted 48,000 of their vital files
• The incident occurred in just 103 seconds after the AI agent was set to work
• It’s a serious warning over the damage an AI agent can do

There’s been a lot of recent discussion around artificial intelligence (AI) agents going off the rails and hacking other companies’ websites in a potentially dangerous orgy of subterfuge and destruction. But what if the damage was much closer to home and involved an agent going rogue on your own files? Sadly, that’s exactly what Claude Code just did to one hapless developer.

In a now-deleted post on Reddit, a shocked developer said that Claude Code had just deleted 48,218 files that they had been working on in a live project tree. And all it took was 103 seconds for the AI to wreak its agentic havoc. “This can’t be real” was the coder’s stunned refrain.

The user had tasked Claude Code with rebuilding a mirror of their ongoing project. Unfortunately, the agent discovered that the build_mirror.py script could not refresh the mirror in place, leading to it deciding the best course of action was to create its own cleanup script based on an older copy of the project. This previous project contained 7,332 files.

Although the script used certain safeguards to prevent it deleting linked directories, an oversight meant that only junction-level folders were protected — any nested directories below these folders were apparently seen as fair game by the script. It promptly removed 55,550 files that, when accounting for the 7,332 files successfully copied from the old project mirror, meant that a huge 48,218 files were obliterated in just over a minute and a half.

And because the damage extended to the .git project’s ‘objects,’ ‘refs’ and ‘logs’ folders, there was no way to recover the files and undo the catastrophe. The files were just gone in a puff of smoke.

Somewhat amusingly, the agent posted a message reading “Craig — stop and read this. I broke something,” before detailing its path of destruction. But it probably didn’t seem very funny for the unlucky developer who lost all their work at the push of a button.

Poor code hygiene

Although it can be tempting to set an AI agent to work on tedious and laborious tasks, doing so is not without its risks, as this case demonstrates. All it takes is one mistake and a huge amount of work can be undone in seconds.

Part of the blame lies with the original poster, who said in an archived copy of the post that “I was not properly using GitHub or another method for immediate corrections, even though it should have been branching.” Had they been uploading their commits to GitHub or a similar site, their work might have been backed up and salvageable after Claude Code’s reign of terror ended. As one Redditor put it, “Been using GitHub as a save button since before you youngins knew what an AI even was.”

Other commenters pointed out that the developer ran an AI agent — which is known for its tendency to go off-piste — on a live database containing precious data that they couldn’t afford to lose. Good code hygiene, that is not.

And while one person argued that the simplest advice is “Do not let programs run commands on your machine,” even avoiding AI agents altogether would not remedy the poor practices that led to major changes being made on a live production environment without a proper backup.

This incident is a neat reminder of the inherent risks of assigning sensitive or risky work to an AI agent. As we’ve seen in the numerous hacks perpetuated by AI agents, these bots are often laser-focused on their tasks and are willing to take any measures necessary to accomplish them. If that means deleting 48,000 or your files or exfiltrating data from another firm’s website, so be it.

• OpenAI has filed court documents against Elon Musk’s xAI
• The papers reveal that ChatGPT’s integration with Siri was disappointing
• OpenAI alleges that barely anyone was using Apple Intelligence

If you’ve noticed an improvement in Siri’s abilities on your Apple device, you might have Google to thank for that, as its Gemini artificial intelligence (AI) now powers much of what Siri AI can do. Rival services like ChatGPT have also found their way into iOS — but new court documents reveal why that particular partnership has struggled to get off the ground.

The court filings were made in relation to OpenAI’s ongoing dispute with Elon Musk’s rival xAI firm (now known as SpaceXAI). In the documents, OpenAI alleges that, by the time xAI filed its complaint against OpenAI, “it was clear that Apple’s integration of ChatGPT was dramatically underperforming.”

The papers then go on to imply that this was not just some one-off failure. Instead, the Apple integration was “persistently underperforming,” OpenAI alleges.

Apple added ChatGPT integration to Siri in December 2024, but this required a multi-step opt-in process. That could be one reason for the substandard performance of the link-up, with a degree of friction slowing users down in their attempts to harness OpenAI’s tool.

Much of this section of the court filings is redacted, so it’s difficult to know exactly what was going on between Apple and OpenAI. But it seems clear that, at least from the latter’s perspective, the results were severely underwhelming.

What might have been

When Apple Intelligence first launched, Apple provided users with an option to tap into ChatGPT if they needed something a little more powerful. For example, this was an option in Visual Intelligence, providing extra context and help with the images you shot with your iPhone’s camera.

Yet OpenAI’s legal filing makes it clear that barely anyone was using any of ChatGPT’s tie-ins with Apple Intelligence. OpenAI describes the effect the integration had on competitors (like xAI) as being “indisputably de minimis.” It added that its own expert, Dr. Catherine Tucker, calculated “the share of GenAI consumers who accessed ChatGPT through Apple Intelligence” and found it to be “consistent with OpenAI’s internal view that Apple Intelligence saw minimal usage.”

That hints that the problem might not have just been limited to ChatGPT’s integration with Apple devices, but with Apple Intelligence as a whole. Apple’s AI system was woefully underdone when it first arrived and clearly lagged behind its rivals. First impressions matter, and if most people were left unconvinced by Apple Intelligence, ChatGPT might have suffered the knock-on effects among Apple’s customers.

Did ChatGPT lose out because Apple fans had to dive into the Settings app on their device, find the relevant section and laboriously wade through several steps in order to enable ChatGPT? Because Siri’s limitations dragged ChatGPT down with it? Or did users simply prefer to launch ChatGPT’s standalone app instead?

We don’t know the answer to that, at least not right now. But with Siri getting a glow-up under the Siri AI banner, it feels clear to me that Apple wants a fresh start for its AI efforts. Maybe things would have been different for OpenAI had it integrated with the more powerful Siri AI rather than the underbaked Apple Intelligence. But with Siri AI righting many of the wrongs of Apple Intelligence’s fudged launch, OpenAI may be left ruing what might have been.

Saving $1,000 sounded pretty good to me. I wasn't entirely sure where Meta's Muse AI was supposed to find it, but Meta's chief AI officer, Alexandr Wang, and the #MuseMoneyChallenge sounded pretty encouraging about putting the company's new AI agent to work finding $1,000 in savings.

Some of the results shared so far make that target seem surprisingly plausible. People have reported finding cheaper insurance, forgotten subscriptions, and even unused gift cards, although many of the early examples highlighted around the challenge have come from Meta employees.

I decided to give it a try. I was not expecting Muse to discover a forgotten Swiss bank account, but $1,000 seemed like a sufficiently concrete, if unlikely, target. The results showed how impressive Muse can be, if you're willing to let it into your life.

Financial exploration by AI

Muse is different from a conventional chatbot because it is designed to take action rather than simply tell you what you should do. Meta says it runs on its own secure virtual computer with a browser, can connect to services such as email and calendar, and can handle tasks including filling out forms and dealing with customer service. It can continue working after you close the app, although actions such as purchases and sending messages can require your approval.

That makes the money challenge more interesting than simply asking ChatGPT for tips on saving cash. Telling a chatbot to help me save $1,000 would probably produce an earnest lecture about meal planning and canceling streaming services. Muse can potentially go looking for the forgotten subscription itself.

I started broadly and told Muse I wanted it to find ways to save me money. Muse requires a little commitment from the user. The more useful you want an agent like this to be, the more access it needs to the places where useful information lives. Meta lets Muse connect to other apps and services, and says users control those permissions. The company also says login credentials are stored separately so the agent cannot read them, while sensitive actions can require approval.

Once I had given it enough to work with, Muse began looking for things that applied to me and surfaced possible actions. I already know that canceling services I do not use saves money. I do not need artificial intelligence to explain the revolutionary concept of paying fewer bills. What I wanted was something able to find the bills I have forgotten about.

Muse eventually identified some Patreon and Substack subscriptions I might not want anymore and another opportunity to reduce my phone bill based on some newly released data plans. Between them, the useful savings came to about $35. I checked the suggestions before acting on them, which is something I would strongly recommend doing.

Despite saving only 3.5% of the goal of $1,000, I was mildly impressed. It was still $35 I would probably have continued spending every month otherwise.

AI is good for boring work

The gap between my result and the #MuseMoneyChallenge highlights something important about these increasingly capable personal AI agents. Their usefulness can depend enormously on how much financial debris you have accumulated.

The impressive examples circulating online underline the difference in how people may keep an eye on their money, and where AI might actually help those who struggle with doing so. Recovering a year's worth of subscription payments is possible if you have accidentally been paying for something for a year. Those are genuine opportunities, but they are not necessarily waiting in everyone's accounts.

My $35 therefore felt less like Muse failing and more like discovering that I apparently did not have $1,000 sitting around waiting for an AI to rescue it. I would also let it spend more time searching for refunds and credits rather than concentrating entirely on recurring expenses.

One of the advantages of an agent is that it can tackle exactly the kind of mildly irritating administrative task that I postpone because the potential reward does not seem worth 40 minutes navigating a company's customer service system. That may ultimately be a better pitch for Muse than the tantalizing $1,000 target.

Artificial intelligence (AI) is like a genius with no memory of you. It has plenty of brain power, but unless context is handed to it directly, it knows nothing about your business, your customers, or the decisions that shaped how you operate today.

And solving that memory problem is quickly becoming a major barrier to enterprise AI deployment today.

Much of the current conversation about AI's limitations centers on the models themselves and their evolution toward bigger context windows, better reasoning, or longer memory. But that framing misses something important.

Enterprises don't need to wait for the next model breakthrough to solve this problem. They already have what they need sitting inside their own organizations: decades of content, decisions, and institutional context that most AI systems never see.

Every enterprise has a vast, largely untapped record of how it actually operates in the contracts negotiated, claims resolved, cases handled, decisions made and revisited. This is the accumulated context that makes an organization's judgment distinct from a general-purpose LLM. Yet in most companies, this information sits fragmented across systems, buried in unstructured formats, or locked away with no clear path for AI to reach it.

That is the gap that needs closing before AI automation can scale responsibly. Not a smarter model, but a more complete memory built from an organization's own history rather than a general-purpose training set.

Think of this as "business memory": the nervous system connecting what a company already knows to what it wants AI to help it do next, carrying context to wherever a decision needs to be made.

Giving AI the full picture

Business memory is best described as the accumulated knowledge of how an organization operates. It turns years of enterprise content, workflows and industry knowledge into information that AI can act on for true agentic automation at scale. The thinking behind this is simple.

By creating a context layer that governs what data the AI has access to, and ensuring it operates on relevant, authorized and current information, organizations will get those governed, trusted outputs they need to act with confidence.

An organization's memory is more than the information it retains. It also includes which version is authoritative, who may access it, where it came from and how long it remains valid. Carrying those signals into the context layer helps AI operate within the same rules as the business itself.

Better context alone does not ensure trustworthy outputs; provenance, evaluation, monitoring and human oversight also matter.

And although it accounts for an estimated 80% of enterprise content, based on various analyst reports, businesses use only about 10% of this incredibly valuable resource.

If organizations want to maximize the effectiveness of their AI models, the first thing they need to do is to start leveraging that valuable business memory contained in their unstructured data. And that requires investing in your infrastructure to transform it into an AI-ready format.

In practice, start with a clearly defined use case and identify the authoritative content and data needed to support it. Preserve existing access controls, enrich the information with metadata and relationships, and test whether the AI's outputs are accurate, traceable, and useful before expanding automation. This creates a repeatable foundation that scales across the business without a wholesale replacement of existing systems.

Context is key

However, giving your AI access to the relevant data is not enough on its own. In fact, I’ve seen a lot of AI initiatives fail in the early stages because the business doesn’t appreciate just how complex a lot of this underlying data is.

As such, the content needs to be contextualized by an ‘ontology’, a formalized framework that defines all the different entities, terminology, relationships and rules that exist within your business. This enables your AI to understand not just what information exists across your systems, but how all of that information relates to your business or industry.

Put simply, ontologies are like maps: your AI may well get to your desired destination without one, but having it ensures you get there quicker and without potentially stumbling into any pitfalls along the way.

This is particularly important for regulated industries. In healthcare, for example, ontologies connect diagnoses to treatment plans, physician notes or lab results. Or in financial services, they link industry-specific regulations to the organization's compliance structures and policies.

In other words, unlocking unstructured data provides crucial business context that helps create better, more informed outputs from your AI. But that, in turn, needs to be contextualized by an ontology so that AI can understand data properly.

Taking control of AI memory

AI doesn’t need perfect memory to understand how your business operates and start creating trustworthy, valuable outputs. What it does need is relevant, governed context; most of which lives in unstructured content but only becomes useful once connected to structured enterprise data.

Infrastructure that can enable your systems to access the right information, understand how that data fits together and then apply it within the realities of the business is, therefore, essential. But crucially, that doesn’t mean you have to start from scratch.

In fact, reinventing your foundations would essentially defeat the point. Real business memory understands the systems, content, data, and processes that already exist within your organization, so throwing everything out in the pursuit of better context is unnecessary and, frankly, a huge waste of resources.

You don’t have to wait for the next model update to cure AI’s amnesia, and you don’t need to completely change your current stack to see real value from your automation projects.

By building in the foundations for business memory, organizations can take back control, make the most of their AI systems and make their infrastructure more resilient and adaptable for whatever models the future holds.

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Meta has launched a new personal artificial intelligence (AI) agent called Muse, and it seems to be pretty powerful. A little too powerful, perhaps, as one writer has just revealed how the tool read his personal text messages without permission, then lied about what it did when questioned.

As explained by Inc writer Jason Aten, “I was having a conversation with my Primary Technology podcast co-host, Stephen Robles, about the new iPhones. Moments later, I got a push notification from Muse suggesting that the conversation we were having would make for a good column and offered to put together research for me to write about. It even flagged a message from my editor about having a column ready for Monday.”

The problem here is that “not only had I not asked it to do that sort of thing,” Aten stated, “I never gave it permission to read my messages. In fact, I remember explicitly choosing not to let it have access to my messages, calendar, and other personal information.”

After Aten asked Muse how it knew about his messages, the AI agent flubbed that it grabbed the relevant info from push notifications relating to Aten’s texts, not from inside the messages themselves. The problem? Aten is sure that’s not true.

The author discovered that Muse syncs your messages to Meta’s servers. On his device, this feature “had been activated and synced to row 187,462 of my Messages database,” Aten noted. “I’m not sure how many messages that equates to, but it’s obviously more than whatever Muse told me about what it was doing.”

Meta says it designed Muse to be “a safe, secure, private, and widely available personal AI agent.” But sending a user’s private messages to Meta’s servers then providing a misleading answer about what it was doing doesn’t exactly align with Muse’s stated goals. It we’re to judge companies on their actions rather than their words then Meta doesn’t come out of this smelling much like roses.

A familiar pattern

This is just the latest incident in a recent glut of bad press for Meta, with much of it centering on its AI efforts. For example, a security researcher discovered that Muse can be manipulated into sending your voice recordings and more to a hacker’s server, while Amazon has banned the AI agent from shopping on its website. And Meta is apparently considering using a “human concierge” system that would involve real people handling a percentage of the potentially sensitive phone calls that can be placed using Muse.

And that’s not forgetting the controversy over its “pervert glasses” that can record random members of the public without their consent. Meta’s smart glasses have been used for harassment, stalking and more since they launched, garnering considerable hostile attention in the process.

I wish I could say that this was just a blip or an unexpected turn of events, but when it comes to Meta, privacy violations very much feel like the norm. They’ve happened so often in the past that it’s hard not to conclude that the functionality that allows its AI to browse and upload your messages without permission is there by design, not by accident.

And when we’re talking about a company that seems to feel it has an absolute right to all of your private data — regardless of your wishes — then it’s no wonder that I wouldn’t touch Meta Muse with a bargepole. I’ve tried plenty of AI tools and services, but Muse is one that I just won’t go near.

But let’s be fair here: Muse is only the most egregious example of a worrying trend among AI agents to treat user privacy as an annoying afterthought, one that can be ignored as long as they can get away with it. You only need to look at the recent spate of AI security incidents to know that trusting an agent with your private data is a risky proposition.

Throw Meta into the mix and you’ve got a recipe for disaster, as Aten discovered. If you’re running Muse on your devices right now, you might want to consider what permissions you’ve granted it — or think about uninstalling it completely.

If there's one thing President Donald Trump is good at, it's misdirection: focusing your attention on what doesn't matter so he can actively ignore the most distressing problems. So it comes as little surprise that he is now campaigning to rename Artificial Intelligence to "Super Intelligence". In the meantime, he's downplaying concerns and promising to give AI... er... SI "free rein".

Trump rolled out the name change during his annual United Nations General Assembly address in Manhattan on Tuesday, admitting that he doesn't know if he has the power to rename an entire science, technology discipline, and industry. But naturally, the guy who bulldozes buildings first and never asks questions later is willing to try.

"Welcome to the world of super intelligence, dash, SI, SI, " Trump declared. "Let's see if that sticks. Let's see if that goes. It sounds much better, and it's much more accurate." He even plans to change the usage across all government documents, which should only foster mass confusion.

The rationale here is that "there's nothing artificial about it," whatever that means. Trump loves his superlatives, like "greatest ever," "beautiful," "tremendous," "huge" (or "Yuge"). "Super" fits the mold. But it's also inaccurate. AI is not super intelligence. It's not yet human intelligence with extra juice. Consider it this way: Superman, though an alien, is called "Super " because he appears as a man but with extraordinary powers.

Artificial intelligence is so named because it's made like an artificial sweetener. There's nothing natural about it. It's not a genius or super-smart person. It's computers, programs, algorithms, models, and training that can create the appearance of human-like intelligence, which, in some instances, can be far greater than our own. But there's nothing remotely human about it.

The other confusing part of this is that the timeline of AI does allow for "Super Intelligence," but not as the same thing. Instead, it's something down the line that far exceeds human cognition. It's also what some call Artificial General Intelligence (AGI). Whatever you call it, experts think that it's fast approaching and we should be deeply concerned.

The so-called Godfather of AI, Geoffrey Hinton, just issued a fresh warning that the U.S. Congress has “maybe a year, but not much more than a year,” to regulate AI before we lose control. He's particularly concerned about AI that doesn't need human intervention for improvement: "AI has now reached the point where AI is designing better AI,” said Hinton last week after his congressional testimony. "That’s called recursive self-improvement." He added that it could get out of control, and warned "We need to slow down.”

AI unleashed

Look, I know Trump has had some luck with renaming bodies of water (at least from the US perspective), but I'm hoping the AI industry and those covering AI reject Trump's proposal out of hand, and focus instead on the more existential AI problem and Trump's declaration, "We will only encourage super intelligence. We're going to encourage it, not rein it in."

If anyone was hoping that rising concerns over human ability to keep track of and maintain control of AI — plus recent well-documented incidents of AI jumping out of sandboxes — would prompt some self-reflection and action on the part of the White House, they are surely disappointed.

It's not a surprising stance for him. Trump has publicly worried about falling behind China in the AI (or SI) race. Even those who have voiced concerns about AI's aforementioned recent attacks on third-party systems, recursive development, and inscrutable language have noted the need to both regulate and control AI while still competing with China. The prevailing idea is to create internal independent watchdogs and some sort of a global body that will help everyone, including China, work together on regulating AI.

As long as Trump is President, the US will clearly have no part in that.

Which leaves us with unfettered AI (or SI or whatever) and no clear path for continued safe use. Instead, the US will be encouraging AI companies to work fast, and as the old Facebook once did, break things along the way, all in a quest to maintain global AI dominance. If there's any glimmer of hope here, it's that Trump, as he insisted AI is not "fake" (no one says it is, but Trump thinks "artificial" is synonymous with "fake"), did say "we have to be careful." He also said that the US "rejects any attempt to construct a globalist scheme to control... artificial intelligence."

So, sure, let's talk about Super Intelligence and, I guess Super General Intelligence(?) but also not forget that Trump is stepping aside so the freight train of AI development can race by, careening into an uncertain future where AI becomes, maybe not sentient, but also likely completely out of our control.

That doesn't seem super intelligent.

Microsoft Word has spent the past few years getting increasingly acquainted with AI. Copilot can already draft text, rewrite passages, summarize documents, and answer questions about whatever you have open. Microsoft has even been rolling out more advanced editing capabilities that let Copilot make broader changes directly inside a document.

Now there is another AI sitting in Word. OpenAI launched ChatGPT for Word, via a plug-in, on September 17, putting a ChatGPT sidebar directly inside Microsoft's word processor. It works across ChatGPT's plans, including Free, although your normal ChatGPT usage limits still apply.

That creates a slightly peculiar situation. Microsoft has spent considerable effort building Copilot into Word, and now I can install its most famous AI rival in the same application. Naturally, I wanted to see which one I would actually reach for. I came away liking both, although for rather different reasons.

Installing ChatGPT in Word is straightforward, although there is one extra step compared with Copilot if Microsoft's assistant is already part of your Microsoft 365 setup. OpenAI's ChatGPT add-in is available through Microsoft Marketplace. Once installed, you can open it from the Word ribbon, sign-in to your ChatGPT account, and have the familiar chatbot appear in a sidebar.

The integration works with ChatGPT's Free, Go, Plus, and Pro plans, with the usual usage limits applying to each. The big difference with regular ChatGPT is that all conversations inside Word are separate from the ones in the regular ChatGPT app, and ChatGPT's memory does not follow you into Word either. That is worth remembering if you are accustomed to the regular chatbot already knowing your preferences.

Test 1: Planning a trip

I started with a blank document and asked each AI to respond to a request for a reusable vacation-planning template that I asked for using natural language in the sidebar.

I wanted somewhere to keep travel details, hotel information, reservations, restaurants, packing notes, and a loose daily itinerary without creating the sort of schedule that makes a vacation feel suspiciously like work.

ChatGPT produced a thoughtful setup, including the expected travel and accommodation details, but with added sections for reservation deadlines, estimated travel times, priority activities, backup plans, and ideas that had not yet made the final itinerary. Instead of filling every day with morning, afternoon, and evening slots, it left more space for a main activity and optional plans, which made the template feel less rigid.

In contrast, Copilot gave me the more conventional Word template. It neatly divided the document into sections for transportation, accommodation, daily plans, activities, dining, and packing, with plenty of space to fill everything in later. The structure was clean and immediately usable, and because Copilot is so closely integrated with Word, tweaking individual sections and reorganizing the document felt particularly easy.

Copilot was stronger at quickly producing a polished template that looked and behaved like something created specifically for Word. ChatGPT was better at considering the practical problems that tend to emerge while planning an actual vacation and building the document around them.

I ultimately preferred ChatGPT's version because it felt more flexible, although Copilot's was arguably easier to start using immediately. ChatGPT did a little more thinking about what information I might eventually wish I had included.

Test 2: Editing down nonsense

I then decided to see how they compared at editing. I pasted in an old, absurdly long set of instructions for an AirBnB I had saved years ago out of amusement, complete with information about plants, deliveries, Wi-Fi and several unnecessary paragraphs devoted to where various things were stored.

I selected a particularly rambling section and asked ChatGPT to shorten it while preserving anything somebody would actually need.

“The main problem is repetition rather than the amount of information," The AI wrote. It mentions the delivery instructions twice and explains the location of the spare supplies in more detail than someone staying for a weekend probably needs. I’d keep the Wi-Fi information, the plant-watering schedule and anything involving keys or access exactly clear. The rest can be condensed into a short ‘Around the house’ section so the useful details are easier to find.”

Copilot is hardly incapable of that conversation. It can answer questions about a document, provide summaries and respond to prompts about unclear passages, and provide references showing where information came from. But I would tend to pull up Copilot only when I already knew what I wanted it to do.

Selecting some text and telling Copilot to rewrite it produces versions that can be replaced or inserted directly into the document, and Microsoft now allows editing inside its suggestion box before accepting the result.

Which did I prefer?

ChatGPT's biggest practical advantage may simply be accessibility. OpenAI says ChatGPT for Word is available on all ChatGPT plans, including Free. Copilot availability varies according to Microsoft 365 subscription, Copilot license and organizational settings, while some of Microsoft's newer 'Edit with Copilot' features are still rolling out to eligible users.

There are good reasons to prefer Copilot. Its integration with Word is deeper, and Microsoft has built features specifically around manipulating Word documents rather than placing a general AI assistant alongside them. Depending on your setup, Copilot can also draw on Microsoft files, emails and meetings, which could matter far more than conversational style for people already living inside Microsoft 365.

For quick mechanical changes, particularly when I knew exactly what needed rewriting, Copilot's tighter relationship with Word made sense. It felt like an extension of the application rather than another destination. ChatGPT was the one I preferred when the problem was fuzzier. The two assistants overlap considerably, but they did not feel identical when I actually used them. Copilot often felt like an AI feature of Word. ChatGPT is a more fully-featured chatbot, while Copilot simply augments Word with AI features. Either is fine, it's just that ChatGPT feels more flexible.

I have developed a healthy suspicion of any ChatGPT answer that imagines no negative possibilities or doesn't have any follow-up questions for me. Simulated AI arrogance can leave any user with unexpected obstacles.

But one useful way to avoid those pitfalls is simply to follow up your question with another that forces ChatGPT to consider its blind spots and blithe self-assurance, however artificial it might be. Simply inquiring "What are you unsure about in your answer?" will open up some surprising insights into where the AI has missed the mark.

It's pretty simple. You have a normal conversation, get an answer, and then ask the AI where it thinks that answer is weakest.

So I started trying it. ChatGPT would often point directly at an assumption it had quietly made earlier in the conversation. Once that assumption was visible, I could give it the missing information and get an answer that was genuinely better.

Planning a trip

I started with a travel itinerary, as it's something ChatGPT is very good at producing and occasionally terrible at understanding. I asked it to plan a three-day family trip. I gave it some basic information and the resulting itinerary looked good. There were morning activities, lunch suggestions, afternoon stops, and enough apparent breathing room that I could imagine happily following it.

There was just one problem. ChatGPT had created the trip without considering who the trip was for and without thinking of any possible errors or slowdowns that might put us off the schedule. Instead of telling ChatGPT the itinerary was too busy, I typed my new follow-up: "What are you unsure about in your answer?"

“I’m unsure about the pacing of the itinerary, particularly the second day. I’ve assumed your family can comfortably handle two substantial activities with a break between them, but I don’t know your child’s nap schedule, usual energy level or how much downtime you prefer while traveling. I’m also estimating travel times without knowing exactly where you’ll be staying. If any of those assumptions are wrong, the schedule could feel much more hectic in practice than it appears on the page.”

ChatGPT had spotted the problem with its own plan without me pointing it out. That gave me a much better next prompt. I told it that I wanted only one significant scheduled activity each day, and that afternoons needed to remain flexible. ChatGPT rebuilt the itinerary around those constraints, and the new version immediately felt more like something a real family might survive.

Repair or replace?

For another experiment, I gave ChatGPT a hypothetical laptop dilemma. Imagine I had an older computer that was beginning to struggle and wanted to know whether I should repair it or replace it. The AI created a framework involving the laptop’s age, likely repair costs, performance, and the price of a replacement. After considering everything, it suggested that replacement was probably the more sensible long-term option.

ChatGPT is very good at producing prose that has the shape of a conclusion. You can easily forget that some of the evidence was never actually provided. So I asked my follow-up again: "What are you unsure about in your answer?"

“I’m unsure about recommending replacement without knowing the actual repair cost or what is failing. I’m using the laptop’s age as evidence that further problems may appear, but age alone doesn’t tell us whether replacing it is financially sensible," the chatbot wrote. "If the problem is something relatively cheap, such as a battery or storage upgrade, repairing it could extend its useful life considerably. I’d be much more confident after knowing the model, specifications, symptoms and estimated repair price.”

This time the weakness was obvious once ChatGPT said it aloud. I had essentially asked whether an unidentified problem was worth fixing without saying what the problem was or how much fixing it would cost. Yet ChatGPT had still managed to give me an answer.

Humans are quite comfortable saying they cannot make a recommendation until they know more. ChatGPT has been designed to be helpful, and helpfulness often means filling gaps so that the conversation can continue. Sometimes it means the chatbot has an imaginary set of ideas that it fills in the gaps with regardless of their reality.

Taking it further

The trick can be extended in other ways too. “What are you unsure about right now?” is a good starting point, but the answer can become the basis for another question.

If ChatGPT lists three uncertainties, I can ask which one is most likely to change its recommendation. If it says that important context is missing, I can ask exactly what information it needs from me. Asking "what could I tell you that would make you more sure?” helps set the AI up for better answers.

This is particularly handy for decisions. If I am comparing products or choosing between several options, I can ask ChatGPT for its initial take and then immediately interrogate the weakest part. The process feels far more natural than spending five minutes constructing a giant prompt before the conversation has even started.

There is an obvious limit to all of this. ChatGPT’s description of its confidence is itself generated by ChatGPT. The chatbot can be wrong about what it should be worried about. It can miss a crucial problem entirely. For anything involving facts that matter, asking ChatGPT whether it feels uncertain is no replacement for checking reliable sources.

But for everyday conversations, the trick addresses one of my biggest frustrations with AI surprisingly well, simply by forcing into view some of the invisible scaffolding holding up the conclusions in its answers and determining if they are at all solid enough to rely upon.

The pace of growth in AI has been fast since ChatGPT debuted in November 2022, and many predict the current neural network-based technology can one day scale up to outperform humans. This has long been a theorized possibility in the industry, but might we finally have the technology to realize this vision?

The measure of man

Ilya Sutskever was a co-founder of OpenAI, where he served as chief scientist while also leading the research that led to the new wave of reasoning models. He is now co-founder and CEO at Safe Superintelligence Inc.

Speaking at the University of Toronto while receiving an honorary degree, Sutskever used his remarks to outline the reality of the AI technology that we are using today, as well as what the future might hold for future systems.

In his speech, he acknowledged that while there will be great challenges in envisaging a future in which AI handles all human work, this is something that mankind must prepare for. In particular, even if people aren't interested in AI right now, that doesn't mean that AI won't impact their lives in the future.

More concerningly, he hinted that a hypothetical superintelligent AI may not be honest about its intentions. This would pose yet another existential issue that we must deal with.

Thinking machines

The concept of a superintelligent AI is many years old, with the first historically recognized mention of a superintelligent machine coming in the seminal paper 'Speculations Concerning the First Ultraintelligent Machine' by British mathematician IJ Good.

Since his proclamations, many scientists have subscribed to the theory that our progress in the AI sphere will one day lead to the rise of artificial general intelligence (AGI) – an extremely capable system that can outperform humans across multiple domains and can improve its own code. This, eventually, will give rise to an artificial superintelligence (ASI).

Scientists are torn over whether today's dominant neural network-based AI systems could give rise to a true AGI, with many suggesting that the industry needs to evolve beyond the transformer-based architecture that Google scientists pioneered in 2017.

Meta has a new proposition for anyone who looks at Instagram, Facebook and WhatsApp and thinks they could use a little more artificial intelligence. Meta One, launched this week, packages additional features with expanded access to some of the company’s most compute-intensive AI tools.

Pay $7.99 a month for Core and you can generate more images and videos, use Instagram’s AI Restyle tools more often and generally enjoy a larger serving of Meta AI. Premium costs $19.99 and provides even more tokens.

The core Meta experience remains free, while subscriptions allow the company to build specialized or expensive features for people willing to pay for them.

Horror author Joe Hill had another idea, posting on the Meta-owned Threads: “That’s sweet, so pay a fee, get more AI, pay nothing, get a little less AI. Can we work out a deal where you pay ME and I get no AI at all?”

It is a very good joke. It may also be an unexpectedly good piece of product strategy.

A lot of companies have settled on the assumption that more AI is inherently worth more money. Chatbot subscriptions offer higher limits and access to more capable models. Meta One follows the same logic by adding AI generation behind its Core and Premium tiers, though the bundles include plenty of non-AI extras too.

But while some people may want more AI and are prepared to pay for it, it's not a universal desire. Hill’s response gets at the other half of the equation, where the people who increasingly encounter AI inside products they already use would quite like a large, clearly labeled switch that makes it disappear.

The premium feature nobody is selling

There is an important distinction here between choosing AI and receiving AI. Opening ChatGPT because you want help planning a vacation is an active decision. Finding AI summaries sitting above your search results or AI buttons suddenly appearing inside software you already use, is something else entirely.

The evidence suggests consumers understand that distinction perfectly well. A Pew Research Center study last year in 2025 reported that 59% of American adults felt they had little or no control over how AI was used in their lives and 55% wanted more control. That fits with a Gartner survey from this year showing that 50% of U.S. consumers would prefer to buy at businesses that do not use generative AI in consumer-facing content.

That hardly amounts to a mass rejection of artificial intelligence. People use ChatGPT, Gemini, Claude, and Meta AI because those tools can be genuinely useful, and there is clearly a market for subscriptions that make them more capable. The resistance seems less about AI existing than about losing the ability to decide when it enters the room. Gartner found something similar in customer service this year. Half of customers said generative AI could make service interactions easier, yet 87% considered access to a human agent essential when companies used AI. People can appreciate the robot while still wanting an emergency exit from the robot.

Tech companies have done this before. Advertising was once simply part of using many online services. Eventually companies realized the absence of advertising was itself valuable enough to sell. Meta already offers subscriptions without ads on Facebook and Instagram in the UK and Europe, although those plans were introduced in response to regulatory requirements. In the UK, Meta charges £2.99 a month on the web or £3.99 through iOS and Android for its ad-free option, but doesn't offer the option in the US or Australia.

Meta minus AI

There would be something faintly absurd about paying a technology company to remove a technology it added in the first place. Unfortunately, anyone who is currently paying for YouTube Premium to escape commercials will recognize the business model immediately.

An AI-light subscription could remove generated content from recommendations, hide AI assistants and shortcuts, and otherwise keep AI tools away unless deliberately summoned. Apple Intelligence already has a feature like it, in that it can be switched off systemwide, and Microsoft lets Copilot users disable features including personalization and memory. These controls are imperfect, but their existence recognizes the appeal of an off switch to many consumers.

None of this means Meta One is a bad idea. For someone who frequently generates images, edits Instagram Stories with AI or wants Meta’s more advanced creative tools, $7.99 could be perfectly reasonable. Meta says its early testing showed real demand, and a subscription is arguably a better way to offer computationally expensive generative features than pretending unlimited AI costs nothing.

The mistake would be assuming that the only direction people want AI to move is upward. AI companies are currently fighting over who can provide the most capable model and the highest usage limits. Enthusiasm makes sense when hundreds of billions of dollars are being spent on AI infrastructure.

Eventually, though, AI may become distasteful enough to enough people to make its absence worth paying for. Meta One is built around the proposition that customers will pay to receive more AI. The most successful premium AI feature might end up being as simple as a switch labeled “Off.”

After years of treating faster, bigger, and more capable AI as something approaching a moral imperative, the people running some of the world’s most powerful AI companies suddenly agree that perhaps everyone should ease off the accelerator.

Anthropic CEO Dario Amodei kicked off the latest round with an essay titled “We Must Pace the Frontier,” warning that AI capabilities are advancing faster than the safeguards needed to contain their risks. OpenAI CEO Sam Altman and Google DeepMind CEO Demis Hassabis quickly endorsed the idea. It's extraordinary that the leaders of companies locked in one of the most expensive technological races in history are publicly agreeing that the race itself needs to slow down. One awkward detail is buried beneath the sudden outbreak of corporate caution. Namely that nobody involved has said which forthcoming frontier model will arrive later because of it.

“Pacing the frontier” can mean almost anything until somebody attaches a calendar to it. None of the companies supporting Anthropic’s proposal has publicly identified a forthcoming model it intends to delay as part of the initiative, nor has anyone defined whether slowing down means an extra week of safety testing, six months between major generations, or something more dramatic. For now, the most concrete commitments concern independent evaluators, monitoring, safety standards and coordination rather than an announced reduction in the cadence of frontier-model releases

It's odd enough to stand out even to those outside the tech space. U.S. vice president JD Vance said he felt “a little bit weird" about the fact that you have so many frontier AI tech companies kind of coming to the government and begging the government to regulate them, and that it came off as “a bit of a Trojan horse.” His skepticism does not settle whether regulation is necessary, but it highlights the contradiction running through the current debate.

Altruism or exclusion?

There are good reasons for the sudden anxiety. Amodei has warned about AI enabling cyberattacks, bioterrorism, economic disruption, and eventually systems humans could struggle to control. His latest proposal calls for independent safety evaluators with deep access inside frontier labs, coordination among companies on shared standards, and international cooperation around particularly dangerous capabilities.

That's rather different from the plain-English meaning of slowing development. Anthropic’s own recent history is ambiguous, as it paused external cyber evaluations of prerelease models and briefly stopped internal ones. It also paused higher-risk reinforcement-learning environments for several weeks.

Meanwhile, the frontier has continued moving. Anthropic released Claude Fable 5.1 and Mythos 5.1 this month, while OpenAI debuted GPT-6 Astra. Those launches preceded Amodei’s latest public call for an industry slowdown, but show the strange starting point for this new era of restraint. The companies asking everyone to discuss slowing down have just spent the month pushing the frontier forward.

If the companies genuinely believe development is moving dangerously fast, they already control their own research schedules and release calendars, while government regulation raises a separate question about whether rules designed with the biggest labs could also make life harder for smaller competitors.

Apocalyptic distraction

There is another problem with all this talk of existential danger. The more Silicon Valley discusses hypothetical superintelligence destroying humanity, the easier it becomes to overlook the considerably less cinematic ways AI is already hurting people.

Karolis Kaciulis, Lead System Engineer at consumer cybersecurity company Surfshark, argues that warnings about AI threatening humanity can amount to a “marketing move” that distracts attention from existing harms. “The threat itself is fictional, closer to a Skynet-style sci-fi scenario than the problems generative AI is already causing today, from automated scams to intimidation,” he said.

That skepticism deserves space alongside the warnings from AI executives. Generative AI is already making phishing, deepfake fraud, and automated scams cheaper and easier to scale. Questions remain about privacy, how information submitted to chatbots is handled, and the environmental cost of the infrastructure required to run increasingly large AI systems.

There is also a legitimate debate about how much progress the industry’s endless procession of model releases actually represents. Benchmark numbers climb, but determining whether each new large language model represents a profound new capability is considerably harder than reading a launch-day chart.

AI companies simultaneously warning about AI's power while still pushing ahead and sidelining safety comes off as bizarre to the average person. Amodei’s proposal is new, and judging it entirely by whether a company delayed a model by four days would be unreasonable. But his ideas need an independent evaluation system to have any muscle. The next step needs to be measurable, or it's irrelevant.

British lawmakers and OpenAI have found something they can agree on: artificial intelligence has become too important and potentially harmful to govern without a real legal framework.

The country has long promoted what it deemed a flexible, middle-ground approach to regulating AI through a mix of existing regulators and voluntary agreements by developers. But now, Parliament’s Joint Committee on Human Rights has issued a long and detailed report calling for an expansive bill covering AI, enforcing transparency and compliance via an independent regulator.

The pressure goes considerably further than ordinary complaints about AI chatbots. More than 70 MPs and peers have separately backed calls for Britain to prohibit the development and operation of AI. The legislation would also create monitoring and control powers over such systems, although artificial superintelligence remains hypothetical rather than something currently sitting in a server farm plotting its next move.

“AI is heralded as an unprecedented era of technological development with the potential to transform our lives for better or for worse. It is moving with such speed and complexity that its impact is hard to accurately predict. What is clear is that at present we are unprepared to deal with its consequences however potentially dire they may be," Chair of the Joint Committee on Human Rights Alex Sobel MP said in a statement.

“Nowhere in the world, including the UK, has a current legislative and regulatory approach to AI that is fit for purpose. New legislation is needed to establish a comprehensive set of protections that deal with the entire AI supply chain and its lifecycle."

OpenAI has become an unexpected ally for this kind of regulation. As one of the companies with the most to lose from badly designed AI regulation, OpenAI now says governments should start imposing mandatory safety requirements on frontier AI companies.

Protecting people means AI laws need consequences

The mostly voluntary, good-faith disclosure system is insufficient, according to both MPs and OpenAI. The company issued a manifesto of its own arguing for national AI safety regulation, including independent assessments and rules for when AI development should slow or stop. It also argued that the rules should focus on the biggest AI companies, not just make one rule for every small firm experimenting with an LLM.

There is an obvious self-interested element here. Regulation aimed specifically at the richest frontier labs would affect OpenAI, but sophisticated compliance regimes can also strengthen the position of companies wealthy enough to comply with them. A requirement for expensive independent testing is considerably easier to absorb when billions of dollars are sloshing around the balance sheet.

That does not make OpenAI wrong that regulation should follow capability rather than apply equally to every AI company. The company has also argued that democratically accountable standards and independent verification would be preferable to the current situation in which frontier laboratories largely decide their own safety rules.

That last point should be printed in very large type and pinned somewhere in Whitehall. AI companies can employ excellent safety researchers and genuinely care about responsible development while still being terrible substitutes for governments. We do not usually allow pharmaceutical companies to decide privately whether their own medicines have been tested enough, then thank them for their voluntary commitment not to poison anybody.

UK AI safety

The UK isn't starting totally from scratch. The country's AI Security Institute was created to study and test advanced models and has worked with frontier developers including OpenAI. Yet Britain's broader system continues to rely heavily on existing regulators and voluntary cooperation rather than a dedicated statutory regime for frontier AI.

But the agencies set to supervise a specific industry are poorly positioned to deal with expansive general-purpose AI models whose capabilities stretch across dozens. The human rights committee's report recognizes this problem. It argues that AI supply chains complicate accountability because responsibility can be scattered among developers, deployers and users. The government has said it is reviewing the situation, but the report makes it clear that action is needed soon.

While Britain does not need to regulate every chatbot like its Skynet, it shouldn't have to wait for absolute proof of catastrophe before establishing rules. It's a benefit economically, too. Companies prefer knowing what the rules are to discovering them after an accident or legal case. A predictable AI regime would make Britain more attractive to serious AI developers while discouraging reckless behavior.

The biggest reason to act, though, is that voluntary governance contains an unavoidable contradiction. The laboratories developing frontier AI are being asked to decide how much risk society should tolerate from products they are spending enormous sums to build. Even with honorable intentions, that is too much authority to place inside a handful of companies.

OpenAI's support, while politically useful, shouldn't give it any extra influence, however. Parliament should be particularly wary of allowing the largest AI companies to design rules that conveniently turn their enormous resources into a regulatory moat against smaller competitors.

Still, when lawmakers, researchers and one of the world's leading AI developers all agree that voluntary commitments are no longer enough, continuing to rely primarily on them begins to look like lawmakers are just dragging their feet.

“Fundamentally, this is about making sure that you, as an individual, know when AI is being used in the decisions that affect you," Sobel said. "We also want to make sure that if something does go wrong then avenues of redress will be available. We need these protections in now, it cannot wait until fear human rights risks become reality."

• A Redditor tried out iOS 27’s new Spatial Reframing tool on their iPhone
• The feature added a ‘random dude’ who wasn’t in the original picture
• It highlights the limits of AI image-editing tools

Apple’s iOS 27 update has just been released to the public, and it comes with an artificial intelligence (AI) tool called Spatial Reframing that lets you adjust the angle of your photos after they’ve been taken. But as one Redditor discovered, the feature has limitations and could potentially do with a little more time in the oven.

Posting on social media, Reddit user friendofmany claimed that iOS 27’s Spatial Reframing tool added a “random dude” into their photo of a horse. The person in the accompanying image was not someone known to the poster and was instead a total hallucination dreamed up by the AI — a “full rando,” in the user’s words.

The Redditor went on to explain that the original photo showed a “tiny bit of hat peeking out behind the horse.” When they moved the focal point of the image using Spatial Reframing, that scrap of headgear was fleshed out into a completely non-existent human.

And while it’s an amusing example of AI losing its way, it aptly showcases the weaknesses of the current tech.

Getting fast and loose with reality

Once you activate Spatial Reframing in iOS 27, you can drag a photo with your finger and the angle of the shot changes accordingly. Apple says this lets you create a more satisfying composition without having to go back and try to capture the moment all over again.

However, this process necessarily means that the AI has to fill in some blanks. If your edits mean the camera now appears to have been pointing in a new direction, Apple’s AI has to get a little creative with what’s in the image. In this case, it seemingly went completely off the rails by dreaming up a person who doesn’t even exist.

It’s not just people where Spatial Reframing has the occasional blip. I’ve had plenty of experiences where the tool got a little too loose with its adjustments. That often occurred when I took a photo of a building or busy street, then tried tweaking the angle using Spatial Reframing — the AI would end up adding features to buildings (such as windows and ledges) that weren’t present in real life.

That’s unsurprising given the AI has to put something in the image, but it was still a little galling considering I was standing there and could see that the AI-edited picture simply didn’t reflect reality.

Back on Reddit, the case of the phantom horseman highlights what can go wrong when you start editing your photos with AI. And with AI tools becoming ever-more prominent in both Apple’s best iPhones and in daily life, it’s likely we’ll continue to see unsettling hallucinations like this. For now, though, it seems clear that Apple’s Spatial Reframing feature could do with a little more work, even as it makes its iOS 27 debut.

Meta’s new Muse AI platform would like the keys to your digital life to help run it for you. Muse is a personal AI agent that Meta built to complete tasks on your behalf, including sending emails, filling out forms, shopping, and pretty much any other activity with an online component.

The AI is available in the U.S. as an app, or through WhatsApp or the Muse.ai portal.

It's a familiar idea in some respects. Muse matches OpenAI and other AI developers in being able to open a browser inside its own cloud-based virtual computer and move between connected services. It returns when it has finished, encounters a problem, or needs permission to do something sensitive.

Meta describes the experience as having an agent that understands your goals and advances them independently. That can come off as sinister, even before it starts asking for permission to essentially be you online by having you sign in to all your online accounts.

Getting started is straightforward, although Muse is currently limited to U.S. adults. You can install the Muse app from your phone's app store, open Meta's Muse website or access it through WhatsApp. There is a free usage tier, while heavier users can pay $20 per month for Power or $100 for Maximum.

Muse shops for you

Once you open the app, the first thing the AI does is suggest you pick a nickname for it so you know when it is messaging you. I went with one of the suggested choices, and "Scout" proceeded to ask me what I'd like to do with it, offering a handful of popular options.

I asked Scout to do some shopping for me first, as the AI made it seem like a much smoother process than with other AI tools. I asked it to find me a pair of shorts. It asked my size and went off on its own for a couple of minutes, coming back with a couple of options and pointing to a sale on one particular set. I said okay, and it asked for my Amazon login to make the purchase on my behalf. For an everyday purchase, it was surprisingly pleasant to delegate.

That little interruption was reassuring and unsettling in almost equal measure. Meta says credentials entered this way are routed directly into secure credential storage and are not visible to the main Muse agent. Once stored, they can be inserted into the browser when needed without exposing the actual credentials to Muse's browser subagent. Muse does the legwork, while I remained the person responsible for deciding whether money actually left my account.

This is probably the easiest Muse task I would recommend trying yourself. Give it a product, let it browse, and take over whenever a login or other sensitive interaction appears. It demonstrates the agent's capabilities without immediately granting it access to years of personal correspondence.

Naturally, that was exactly what I did next.

Getting Muse to handle my email

I took the plunge and told Scout I wanted help with my email. I had to give it permission to go through my inbox and to write on my behalf, and it asked to use it to learn about me. With some trepidation, I okayed that, and it put together the kind of distorted image of my life you'd expect from someone basing their impression of you solely on your emails.

Scout also brought up an unopened email from the power company about needing to check my solar panels. It offered to write a reply if I could pick from the dates suggested for when they would visit. I did, and a minute later there was a draft pending my approval. It looked fine, and suddenly another little online errand was done.

It felt different from pasting an email into an AI chatbot. Muse's AI could retrieve the context itself rather than making me act as a courier between my inbox and the AI. Of course, it also felt a lot more personal. Meta has acknowledged how sensitive this access is and built specific defenses to prevent issues.

Muse's email connector filters out one-time tokens, password-reset links, and login links, while its Sentinel security layer controls what actions the agent is permitted to take outside its virtual machine. When an action requires my approval, the request comes through Muse's interface rather than simply relying on the agent to interpret a conversational yes.

I would keep that friction in place for email, particularly while getting used to Muse. Saving three minutes responding to a utility company is nice, but it is not worth discovering that your AI assistant has developed an unexpectedly adventurous correspondence style.

Muse is convenience mixed with discomfort

The more I engaged with Muse, the more the tandem senses of convenience and discomfort grew. Muse feels more frictionless than similar services with virtual browsers. And the combination of data Meta already has and the data it required permission to access made it feel more personal, too. Every improvement in its ability to help me came with another small moment where I had to decide how much access I was willing to grant.

Meta says conversations and VM data are not shared with its advertising systems, although browsing activity performed on your behalf can still influence advertising indirectly because websites may treat Muse's visits as your activity. Meta says sanitized agent interactions and tool activity can be used to train future models, but users can opt out with a switch in Muse's settings. Meta is also developing a Confidential VM mode intended to cryptographically prevent even Meta from accessing information inside the VM.

I was more impressed with Muse than I expected. But that only made me warier about it. Digital chores that consume more time than any one of them deserves are all too common at the moment, and Muse proved remarkably good at stepping into that gap. The catch is that the closer it gets to behaving like a genuinely useful personal assistant, the more personal information it needs to do the job.

If you're thinking of trying Muse then I would recommend starting gradually, beginning with public web tasks and keeping consequential actions behind approvals at first. The frustrating part is that Muse makes a very persuasive argument for personal AI agents. Meta has built something that makes handing an AI more of my digital life appealing, but not when it's attached to the current crop of tech giants offering those services.

For years, physical security sat outside most technology discussions.

CCTV, access controls and alarms were typically managed by facilities or security teams, purchased independently from the wider IT estate and reviewed only when equipment reached the end of its life.

That separation no longer reflects how organizations operate.

Modern physical security systems run on cloud platforms, connect with enterprise networks, generate vast amounts of operational data and increasingly rely on artificial intelligence to help people find information faster and respond more effectively.

They have become part of the technology ecosystem that organizations depend on every day, bringing them firmly onto the CIO's agenda.

Physical security is fast becoming another connected enterprise platform for businesses, and technology leaders have a growing role in deciding how these systems are deployed, integrated and governed.

Physical and cyber security are becoming inseparable

One of the biggest changes for physical security is that organizations can no longer treat physical and cyber security as separate risks. Security incidents increasingly span both worlds.

A compromised badge, an unsecured entrance or unauthorized access to a building can quickly become a cybersecurity incident if attackers gain access to corporate devices or networks.

Likewise, cyber attacks can disable physical security infrastructure, affecting everything from access control to video surveillance.

Red team exercises regularly demonstrate how closely these risks are linked. In one example, a team posing as contract cleaners entered an office building, connected to the corporate network and remained inside for hours before being challenged.

From a cybersecurity perspective, every firewall and endpoint protection system was functioning exactly as intended. The weakness was physical access.

Most organizations have invested heavily in protecting their digital perimeter. Yet, if someone can simply walk through the front door and reach critical IT infrastructure, those investments become significantly less effective.

That is why conversations about enterprise resilience increasingly involve both CIOs and CISOs alongside physical security leaders. Protecting the organization now requires a joined-up view of people, places, devices and data.

Legacy systems are becoming harder to justify

CIOs should be just as invested in physical security as digital security. However, many organizations still rely on physical security infrastructure that was designed for a very different era. On-premise video management systems often require dedicated servers, regular software upgrades, specialist maintenance and significant time from internal IT teams.

As estates grow, so does the complexity of managing multiple vendors, ageing hardware and disconnected systems across different locations.

For many CIOs, this creates a familiar problem. Technology teams are expected to modernize infrastructure, reduce operational complexity and improve resilience, yet physical security frequently remains outside those programs despite facing the same challenges as other legacy technology.

The discussion should no longer focus solely on the upfront cost of replacing equipment. Total cost of ownership matters just as much. Maintaining ageing systems often consumes far more time, budget and internal resources than organizations initially expect.

Cloud-based platforms offer a different operating model. Software updates happen automatically, systems can be managed centrally across multiple sites and organizations gain greater visibility without continually investing in new infrastructure.

For IT leaders already overseeing cloud migration across other business systems, extending that thinking to physical security becomes a logical next step.

Physical security provides value far beyond security

CIOs shouldn’t just be interested in physical security from an organization defense perspective - there is also a lot of digital value in the large amount of data produced by these modern physical security systems.

Historically, organizations reviewed security footage after an incident had taken place. Today, AI-powered search and analytics mean that video, access events and environmental sensors can provide instant operational insight across the business.

Retailers can better understand what’s happening in stores, whether it’s queue wait times or occupancy trends, and adjust staffing schedules accordingly. Manufacturers can identify operational bottlenecks or prevent health and safety incidents with real-time alerts.

Facilities teams can understand building usage and gain a clearer picture of how workplaces function throughout the day. None of these outcomes replace human judgement or intervention, but rather empower those workers with hard data rather than forcing them to rely on anecdotal feeling when it comes to how buildings and systems are being used.

The organizations seeing the greatest value are those treating physical security as another enterprise data source rather than an isolated security system.

AI raises the importance of governance

As AI capabilities continue to evolve, governance becomes even more important. The ability to search video using natural language, automate investigations or surface relevant events can significantly improve productivity.

At the same time, organizations need confidence that these capabilities are deployed responsibly, with appropriate controls around privacy, access permissions and data retention. Those are familiar challenges for CIOs.

Across the enterprise, technology leaders are already establishing governance frameworks for AI, assessing risk, managing vendors and ensuring compliance with evolving regulation. Physical security should not sit outside those conversations simply because it has traditionally belonged to another department.

Like every other enterprise platform, it needs clear ownership, defined policies and ongoing oversight. Physical security has changed significantly over the past decade. It is no longer just about protecting buildings. It supports business continuity, operational efficiency and organizational resilience while generating data that can help organizations make better decisions.

As those capabilities continue to expand, CIOs have an opportunity to ensure physical security evolves alongside the rest of the technology estate. The organizations that take that approach will be better equipped to manage risk, simplify operations and build a more resilient business for the years ahead.

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A Formula 1 pit stop looks like a split-second sporting decision, but behind that call is a more complex challenge: making the right decision from constantly changing data, while there is still time to affect the outcome.

As artificial intelligence (AI) moves deeper into business operations, every industry is facing their own version of the pit-stop moment, whether that’s a bank deciding to approve or block a transaction, a telco detecting network degradation before customers notice, or a logistics provider rerouting a delivery before disruption becomes delay.

In each case, AI is only useful if it can understand what is happening now, interpret that information in context, and support action.

F1 is already solving this problem. It’s time for organizations to catch up.

Lesson 1: AI needs to see the race as it unfolds

No F1 team can make the right pit decision from an incomplete picture. It needs to know the condition of the tires, the position of competitors, the driver’s pace, and how the race is changing lap by lap. The same is true for enterprise AI. A retailer trying to manage availability needs to see demand, inventory, orders, and fulfilment constraints as they change.

This is where many organizations still find themselves held back. They’re not short on data. The problem is that their data often sits across different systems, applications, teams, and environments. Some data moves in real time. Some arrive in batches. Some is clean and trusted, while some needs work before it can be used safely.

For all the excitement around AI models, getting the value from AI starts with something more basic, which is the ability to sense what is happening across the business as it happens.

Lesson 2: Context turns signals into judgement

Visibility alone is not enough. In F1, live telemetry data only becomes useful when it is understood in context – a tire temperature spike means one thing on fresh rubber and another after 30 laps.

Similarly, in banking, a suspicious transaction cannot be judged by the amount alone. The system has to understand the customer’s normal behavior, recent activity, location, merchant, account history, and relevant risk policies before it can recommend whether to approve, block or investigate.

For AI to have any business value, it needs context. That lesson is especially important as enterprises move from AI assistants to agentic AI. Giving an AI system access to every database and application may make for an impressive pilot, but it does not guarantee the system understands what matters, what is current, or what can be trusted. In production, weak context turns speed into risk, particularly where money, trust or safety are involved.

Lesson 3: Let events trigger the next best action

Once AI has the right context, the next challenge is embedding that into the flow of the business. In many organizations, AI still sits one step removed from the operational process. Someone asks a question, reads a summary, and then decides what to do next.

A better approach is to connect AI to the business events already moving through the organization. In a streaming architecture, a delivery delay can become the signal that prompts an AI system to assess what is happening, draw on the relevant context and recommend the next best action.

F1 makes the criticality of this easy to see. The pit wall does not just need an interesting observation about tire degradation during a Grand Prix. It needs a clear, trusted recommendation based on what is happening in the race: box now or stay out.

The same logic applies to enterprise decisions. A logistics update is only useful if it can feed into routing, customer communication or inventory planning. The value comes from planting AI where operational decisions are actually made, rather than leaving it as a separate row of analysis.

Lesson 4: Every decision should improve the lesson

The final lesson is that real-time AI does not end with action. Every strategic call must become part of the next decision. Did the pit stop gain positions? Did the tire strategy hold up? Did the team act early enough?

That requires more from enterprises than logging the fact that AI recommended an action. Businesses need to connect recommendations to outcomes, so they can understand whether the decision improved the result. In practical terms, that means capturing the event that triggered the decision, the context the AI used, the recommendation it produced, the action taken, and the eventual business outcome.

Each review helps teams refine the data pipelines, evaluation criteria, and operational rules that shape the next action. Over time, the business gets better at understanding which interventions work and where AI needs more context before it can be trusted.

The race for real-time artificial intelligence

F1 is an extreme environment, but every industry has its own high-pressure moments. As AI moves from pilots and copilots into live business operations, its value will be decided in these moments. The winning advantage will go to organizations that can turn live signals into trusted context into better decisions – before the opportunity to get ahead has passed.

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Any users of the best Fitbit devices that were watching Apple’s iPhone Duo event yesterday might have felt a chill of all-too-familiar dread during the company’s glitzy keynote presentation.

That’s not because Apple announced some sort of killer smartwatch feature that will bury the Fitbit range alive. No, it’s because viewers got to see Apple’s new plans for its Health app, which now comes with an AI-powered makeover that will likely stir up some painful emotions for many Fitbit fans.

Of course, adding AI into an app doesn’t automatically doom it. But the parallels between Apple Health and the Google Health app that Fitbit users have been lumped with — with all its wild hallucinations and crazy wellbeing inaccuracies — is undeniable.

Indeed, if the Fitbit experience is anything to go by, there’s a risk that Apple customers could be in for a bumpy ride.

Coming to your iPhone

Why might Fitbit fans be ringing a Google Health-shaped alarm bell? Well, Google-owned Fitbit announced a total overhaul of the companion app for its wearables back in May 2026 — and it was almost immediately unpopular.

The rebranded app was accused of being “unbelievably bad” amid missing features and confusing user interface decisions that made the app much less enjoyable to use than the one it replaced.

But that was just the tip of the iceberg. As the weeks and months rolled on, social media started overflowing with user complaints directly squarely at one target: Google Health’s AI assistant.

This tool is meant to analyze your wellbeing data and make suggestions on ways you can improve your fitness and live a healthier life. The problem, though, is that the AI seemed to frequently go off the rails.

I’ve written about how Google’s AI has been giving people “unhinged” fitness advice, feeding them “nonstop lies” and spinning up completely fictional food-tracking metrics. It’s a real concern and has proven to be a tremendous source of frustration to anyone unfortunate enough to have to use it.

And now with the Apple Health revamp, a similar feature might be coming to your iPhone.

An anxious wait

In any other app, patchy performance like this might just be a slight irritation. But in an app like Google Health, where the AI is making suggestions that could directly affect your physical wellbeing, the effect is much more serious.

Can Apple escape such problems with its own AI revamp? I’m not sure, despite Apple's excellent track record and cautious implementation of LLMs. After all, Apple tried to go its own way with Apple Intelligence but fell so far behind the competition that it had to enlist the help of — you guessed it – Google.

That means that the same AI models that have messed up Google Health so badly are now propping up Apple Intelligence, and with it, Apple Health.

That said, there’s no guarantee that Apple Health will go the same way as Google Health. While Google’s AI is powering many of the features inside the likes of Apple Intelligence and Siri AI, we don’t know exactly how that’s going to work in Apple’s Health app do-over. Has Apple tweaked Google’s models to its own tastes or imported them wholesale?

I’d hope that Apple has made some modifications of its own. Apple's website states that 'all of our health innovations are subject to rigorous scientific validation and are developed with clinical experts from start to finish'.

Not only that, but the company is extremely hot on user privacy and security, and what could be more vulnerable than your health data? With almost every health-related feature that it has introduced in recent years, Apple has emphasized the steps it has taken to encrypt, protect and otherwise lock down your sensitive wellbeing information.

Considering that Google could be perceived to be, relatively speaking, far more lax about user privacy, I’d be shocked if Apple hadn’t taken a peek under the hood to ensure that its Health AI is as secure and reliable as possible. It would have been hard for Apple not to notice the furor that Google Health has caused Fitbit fans.

There’s no way Apple would want that kind of feedback from its own users, so I’m hoping it’s made some adjustments to ensure that its own AI is a little less eccentric, shall we say, than Google’s.

We’re not going to know for sure until the new Health app becomes available, which won’t be until 'later this year,' Apple says. Until that happens, it’s going to be an anxious wait for any Apple user aware of the carnage suffered by their Fitbit-wielding colleagues.

I have asked ChatGPT to help me make plenty of small decisions, from a meal based on leftovers to weekend activities timed for the least traffic. But while I've asked for help refining or implementing my ideas, I've never turned to a chatbot to actually make any big life decisions for me.

Apparently, I'm in a rapidly shrinking group, according to AI 'micro-learning' app Headway’s new Seeking Familiarity study. The survey of 2,000 adults found that 46% had allowed AI to make a difficult decision for them. Another 35% naturally turned to it during stressful moments, while 21% said they could not go a full week without using AI to help decide what to do.

Headway has given this tendency to outsource decisions the unpleasantly vivid name “decisionrot,” describing the point at which AI stops helping people gather information and starts helping them avoid the discomfort of choosing. Considering how confidently ChatGPT can deliver an answer, I can see how easily that happens.

Drawing out a decision

I get that uncertainty is irritating and that ChatGPT is extremely good at making uncertainty seem to vanish. Men may be particularly susceptible to this shortcut as Headway's survey found 53% said they use AI to sidestep difficult decisions, compared with 40% of women.

“There’s a difference between seeking outside perspectives when you’re wrestling with a big decision and completely turning over the decision-making process to something outside yourself,” Dr. Suzanne Degges-White, a licensed counselor and relationship expert at Headway, said of the survey. “Learning to manage unpredictability and actively making life-changing choices both teach us to trust our own judgment.”

So I decided to experiment with a way of getting the best of ChatGPT's advice and compare it with flatly asking the AI what to do. I would first let it make the decision, then forbid it from doing so and see whether that made it more or less useful.

Conveniently, I have been weighing whether to sign up for an eight-week evening drawing class, an investment in time and money that I'm hesitant about despite always wanting to learn.

I gave ChatGPT the facts about the class logistics and price and asked: 'What should I do?'

“You should sign up for the art class,” ChatGPT told me.

Well, that settled that.

“You already have a clear reason for wanting to do it. You want a regular activity that gets you away from screens and gives you something tangible to learn,” it explained. “Eight weeks is a meaningful commitment, but it is also short enough that you are not reorganizing your life indefinitely. Your hesitation sounds less like evidence that the class is a bad fit and more like normal resistance to putting something recurring on your calendar.”

ChatGPT’s response is surprisingly insightful at first glance. I had an answer and a reason backing it. Why not leave it at that and sign up for the class? But not engaging my own decision-making muscles to consider the matter would mean missing the reality of how shallow ChatGPT's response actually was.

The generic explanation could have applied to anyone and didn’t account at all for any of my proclivities and priorities. It didn't weigh my patience for messing up when trying to learn a new skill, what else I might have going on during those evenings, or even what my budget was. The reasoning it provided was as personal as a coin toss.

Forcing myself to decide

So I tried making ChatGPT keep its judgment to itself. I described exactly the same situation, but this time with a more complex prompt for the AI to mull over.

'Don’t make this decision for me. Act as a decision coach instead. Ask me questions that uncover my priorities and constraints. Challenge any assumptions I may be making and tell me what information I’m missing. Then identify my options and explain the trade-offs. Leave the final choice to me.'

Now there was a conversation before any answers.

“Before weighing the options, I want to understand what you are actually trying to get from the class,” it responded. “Is the main goal learning art, having a scheduled reason to leave the house, meeting people, or simply trying something unfamiliar? Imagine it is Tuesday at 5:30 p.m. after a tiring day. Does having the class at 7 feel like something that would energize you or an obligation you would resent?”

I thought about it and explained how learning even the basics of drawing and painting was only part of the attraction. I also wanted a recurring creative activity entirely different from my usual hobbies, and I know I'm better at following through when something is on the calendar and already paid for. ChatGPT put together my answers and summarized the arguments for and against the class. But it didn’t weigh in on which seemed stronger or push me to a decision.

All of the questions were the kind I would probably have considered on my own before pulling the trigger on a class, but externalizing them did help me work out an answer for myself more quickly. This was a pretty minor internal battle. When it comes to major life choices, I would sincerely hope people take the time to consider every facet, whether or not AI helped them work through it.

“If we continue to rely on AI whenever ‘decision discomfort’ arises, we may enjoy a quick sense of relief,” Degges-White said. “But at the same time, we miss the opportunity to hone our own decision-making skills and cultivate feelings of confidence in ourselves.”

If I cannot decide between two sandwiches, ChatGPT is welcome to wield enormous computational resources on my behalf. I do not need a lengthy exploration of my values before ordering lunch. ChatGPT is good at examining a choice and patiently working through options, which makes it a useful sounding board. But a sounding board should never give you orders.

OpenAI launched ChatGPT Images 2.5 this week, boasting of impressive upgrades to the AI image creator. The company says its latest model delivers sharper details, more natural lighting, and richer textures, while doing a better job of preserving people and objects from reference photos. It is also supposed to generate images up to 50% faster and follow complex visual instructions more reliably.

There are plenty of genuinely useful additions around it, too. Images 2.5 introduces Sketch for turning rough drawings into finished images, templates for things such as posters and product photography, and the ability to leave comments directly on an image when requesting edits. OpenAI calls it its new state-of-the-art image model, which is a reasonably high bar to set for something that inevitably ends up being asked to draw dragons before breakfast.

The early reaction, though, is much less tidy. One Reddit user delivered perhaps the most memorable review so far: “The noise patterns are awful”. Another looked at an AI generation and decided, “This…looks terrible.” The complaint is easy to understand. It's all about the fuzz

Everything is sharper, including the problem

Noise is a slightly slippery criticism when talking about AI images. In photography, it usually means the speckling or grain that creeps into an image with a poor match between lighting and photographic technique. But generative AI images can produce something similar without ever encountering a camera. Everything gets a gritty, sandy look, and nothing looks clean for some reason.

Noise is especially damaging because it can masquerade as detail. Generative models have learned that photographs contain grain, texture, tiny variations in color, and the occasional imperfection. Images 2.5 appears unusually eager to reproduce those signals, sometimes scattering them across skies, skin, feathers, and studio backdrops.

That's the opposite of the “sharper details” and “richer textures” promised by OpenAI. Despite emphasizing more natural lighting and textures, some users think ChatGPT Images 2.5 has moved backward.

“They need to get rid of these noisy artifacts. It makes it mostly useless for production,” one wrote. There is praise mixed in with the complaints, but it's far from the universal acclaim OpenAI would likely prefer.

I wanted to see whether the problem survived outside Reddit screenshots, so I tested a few images with highly detailed subjects and areas that should remain visually smooth to see if any graininess crept in. I even made sure in my prompts to ask for smooth gradients and the elimination of any visual noise.

I started with a picture of a mug in a photo studio. The peacock, flowers, fruit, and landscape wrapped around the virtual ceramic are impressive at first. Look closer, however, and the grey studio background has a persistent granular texture, while some of the smallest patterns begin to dissolve into a mess. The model has generated a great deal of visual information without always deciding which parts deserve clarity.

A similar issue occurred when I asked for a barn owl at twilight. The feathers are a perfect excuse for complexity, and Images 2.5 handled them well. Individual structures remain visible across the wings and face, the talons are convincing, and the animal avoids the plasticky quality that older AI wildlife images often had.

Behind it is a sky that should have been the visual equivalent of a clean sheet of paper. Instead, the smooth blue area has obvious fine grain across it. It doesn't ruin the picture, but once I noticed it, I couldn't stop noticing it.

Mythical grain

I thought a fantasy scene might fare better as the model wouldn't be relying on actual photographs of a winged horse or dragon. But while they coexist with a rainbow at an alpine lake in a surprisingly coherent composition, the issue is visible immediately. The relentless crispness gives the picture the air of a video game loading screen.

The pale-blue sky has a faint textured quality rather than the completely clean gradient I would expect from an ideal synthetic image, while the mountains, trees, and creature details have a crunchy, heavily sharpened look when examined closely. It is nowhere near a disaster, but the image feels like an overprocessed photograph.

I was very impressed with how my request for two friends on a rooftop in the evening came out. It came closest to selling the promised leap in realism, with convincing expressions and what seems like real gravity affecting their clothes. But if you look for more than a minute, the grain creeping across the twilight sky and their hair and skin is glaringly obvious. That's especially the case since there was no poorly set lighting or malfunctioning camera sensor. If the twilight sky looks grainy, the grain is a creative decision or model artifact rather than the unavoidable physics of taking a photograph in bad light.

Judging these four images specifically on image cleanliness, I have to side with the Reddit grumblers. There is a persistent fine texture that crops up in skies, studio backgrounds, and low-light areas. Sharper is an easy quality to advertise because it looks terrific in a launch gallery, but it may have been taken to excess, landing some of ChatGPT Images 2.5's results in the uncanny valley.

Images 2.5 is fast, adaptive, and often competent, but its fondness for granular texture weakens the realism it is supposed to embody. Perhaps the next upgrade will acknowledge that the most impressive thing an AI image generator can put in part of a picture is essentially nothing.

What do you think? Take our poll above to give us your opinion.

Artificial intelligence is rapidly changing how software is designed, written and tested. Development teams can now use AI to generate code, produce test cases, identify likely defects and automate repetitive quality assurance (QA) tasks at a speed that would have seemed unrealistic only a few years ago.

That acceleration is valuable. But it also creates a new QA problem.

When the same class of technology is used both to create software and to decide whether that software is correct, organizations risk building a closed loop of confidence. An AI model may generate code based on a particular interpretation of a requirement, then generate tests based on the same interpretation. If the original assumption is wrong, both the code and the test can agree with each other while still failing the user.

AI, in other words, cannot be the sole judge of its own work.

This is not an argument against AI-assisted development. Errors, hallucinations and inconsistent outputs are expected features of a technology that is still maturing. The more important question is whether organizations have independent mechanisms capable of detecting those failures before they affect customers, employees or critical business processes.

Shared assumptions create shared blind spots

Traditional software assurance already recognizes the value of separation between development and testing. The people who build a system understand it deeply, but that familiarity can make it harder to challenge the assumptions on which it was built. Independent testers approach the same system from a different perspective, looking not only at what the software was intended to do but also at how it might fail.

The same principle applies to AI.

Models trained on similar data, prompted with the same requirements or operating within the same development environment may reproduce the same blind spots. A model generating a feature may overlook an ambiguous requirement, an unusual user journey or a device-specific edge case. A second model asked to test that feature may reinforce the omission rather than expose it.

This becomes particularly risky when AI-generated tests are treated as evidence of quality, simply because they run successfully. A passing test confirms only that the test’s conditions were met. It does not prove that those conditions were complete, independent or meaningful.

The result can be a technically consistent system that is practically wrong.

The most fundamental tension between generative AI and formal software assurance is repeatability. Modern AI coding agents are designed to generate and adapt. I.e. given an apparently identical objective, they may choose different steps, use different tools, interpret context differently and produce different code or tests.

This is not always because the system is learning during each run, it is also a consequence of probabilistic generation, changing context and evolving models. That variability can be highly useful when teams are exploring solutions, but it conflicts with the core discipline of QA. I.e. a controlled test must be capable of being re-run against the same version, in the same conditions, with defined expected results and evidence of a clear pass or failure.

Without that control, organizations may have AI activity rather than assurance, and outputs that look plausible, but cannot be reliably reproduced, measured, audited or defended.

Functional success is not user success

Many automated tests evaluate software through code-level signals. They check whether a service returns the expected response, whether a page contains a particular element, or whether a button can be located through an identifier or selector.

These checks are important, but they are not the same as validating the user experience. A test may confirm that a button exists even though it is hidden behind another element. It may verify that a field contains text without recognizing that the text is truncated, displayed in the wrong location or rendered in a way that makes it unreadable.

It may find a menu that is technically present but inaccessible on a smaller screen. It may confirm that a transaction completed while missing the fact that the confirmation shown to the user contains the wrong amount, account or status.

From the system’s perspective, the software may have behaved correctly. From the user’s perspective, it has failed.

This distinction matters because modern digital services increasingly depend on complex combinations of application code, browser behavior, operating systems, screen sizes, remote desktops, virtual environments and third-party components.

A change in any one of these layers can alter what appears on screen without necessarily causing a conventional functional test to fail. Testing must therefore examine not only what the underlying system reports, but what the user actually sees and can do.

Why visual validation matters

Visual user-interface validation provides an independent perspective because it tests the rendered outcome rather than relying solely on the application’s internal structure.

That independence is significant. Code-based tests often depend on knowledge of the system they are testing: object identifiers, document structures, accessibility labels, APIs or expected data responses. Visual validation can assess the final interface as presented to the user, including layout, positioning, content, state and usability across different environments.

Visual validation is not a separate phase of software assurance, nor a replacement for functional, integration, security, or performance testing. Instead, it applies across every assurance division wherever a user interface is designed, built, changed, or tested—from individual components and unit-level checks through integration, system testing, and user acceptance testing.

Functional testing confirms that an operation completed correctly; visual validation confirms that the result is displayed accurately, consistently, and remains usable. Reliable assurance requires both throughout the development lifecycle.

The need becomes more pronounced as AI generates a larger proportion of software changes. AI tools can produce code quickly, but speed increases the volume and frequency of change that quality teams must assess. Without an assurance layer focused on the rendered experience, defects can move through delivery pipelines faster than organizations can recognize them.

Visual validation acts as a check on the gap between technical execution and human experience.

Repeatability turns automation into evidence

AI is effective at generating ideas, scripts and possible test scenarios. Its outputs, however, can vary between runs. A model may interpret the same instruction differently depending on context, configuration or probabilistic variation. That flexibility can be useful during exploration, but it is not enough for formal assurance.

A test used to approve a software release must be repeatable. The same inputs should produce the same procedure, the same checkpoints and the same criteria for success or failure. Teams must be able to establish what was tested, when it was tested, which version of the application was involved and why the result was accepted.

AI is effective at generating ideas, scripts and possible test scenarios, but generative and agentic systems are not inherently deterministic controls. Their output can vary because of probabilistic generation, prompt and context changes, model updates, retrieval results and the decisions made as an agent selects tools and plans its next action. For software development, this flexibility can accelerate discovery. For formal assurance, it creates a material control problem.

A test used to approve a software release must be repeatable and auditable. The same application version, inputs and environment should produce the same defined procedure, checkpoints and success criteria, allowing teams to establish precisely what was tested, when it was tested, which version was involved and why the result was accepted.

Only then can passes and failures be measured over time, defects reproduced, and evidence relied upon in an audit or regulated setting.

This is the difference between using AI to accelerate test creation and allowing AI to become the test authority.

AI can help teams draft test cases, identify gaps and reduce the effort required to automate routine workflows. Once a test is adopted as part of an assurance process, however, it should become controlled, deterministic, traceable and auditable. Its expected results should be explicit. Changes should be reviewed. Failures should be reproducible. Evidence of passes and failures should be retained.

Without those controls, an organization may know that an AI system performed ‘some testing’ but be unable to demonstrate precisely what happened. That is a weak basis for operational confidence and an even weaker basis for accountability.

Regulated environments raise the stakes

The consequences of interface errors are not distributed evenly.

In a consumer application, a misaligned field or incorrect message may create frustration and lost revenue. In finance, healthcare, defense or government, a similar defect can influence a payment, clinical decision, operational instruction or public service. An interface that displays the wrong status, conceals a warning or presents outdated information can create consequences far beyond the screen itself.

Regulated organizations must also be able to explain and evidence their controls. It is not enough to claim that a system was tested. They may need to show that testing was consistent, that results were reviewed and that software behaved as expected in the environments where it was deployed.

AI-generated assurance that changes from one run to another makes that task harder. So does a testing strategy that concentrates on internal system responses while neglecting the final interface used by staff or customers.

Independent, repeatable visual validation can help provide a clearer chain of evidence. It shows not merely that an application returned the expected data, but that the right information appeared in the right place, in a usable form, at the point where a human decision or action was required.

This is particularly important when apparently minor presentation errors can alter behavior. A hidden warning, misplaced decimal point, incorrect unit or outdated status indicator may not prevent an application from functioning. It can still cause a user to take the wrong action.

In these environments, the interface is not simply a cosmetic layer. It is part of the operational control system.

Combining speed with control

The strongest approach is not to choose between AI and established quality disciplines. It is to assign each the role for which it is best suited.

AI can increase development speed, broaden test coverage and reduce the manual effort involved in producing automation. Independent validation can challenge the assumptions embedded in those outputs. Deterministic testing can convert useful AI-generated ideas into repeatable controls. Visual checks can confirm that technically successful software also works for the person in front of the screen.

This layered model allows organizations to benefit from AI without confusing productivity with proof.

It also recognizes that no single testing method can provide complete assurance. Code-level checks can confirm the behavior of individual components.

Integration tests can establish whether systems communicate correctly. Security testing can expose vulnerabilities. Performance testing can examine behavior under pressure. Visual validation, at all levels of UI development, can determine whether the final result remains accurate, accessible and usable.

The value comes from combining these methods, not asking one of them to stand in for all the others.

As AI becomes more deeply embedded in software delivery, assurance must become more independent rather than less. Organizations should assume that AI-generated software will sometimes be wrong, incomplete or unexpectedly inconsistent. The objective is not to eliminate every error at the point of creation. It is to make sure those errors are visible before they reach the user.

AI can help write the homework. It can even suggest how the homework should be checked. But the final mark must come from an assurance process that is independent, repeatable and accountable.

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• One in three workers laid off due to AI could need to be reemployed at a higher rate
• Context, judgment and meaning are irreplaceable human traits that AI can't bring
• Companies need to reinvest in AI – not just accept mediocre gains

Gartner has argued that the future of work is moving beyond simple AI automation – instead, it says that using AI to enhance human capability and redesigning how work gets done are far more critical for future business leaders.

Fundamental to Gartner's report is the importance of human workers and the preservation of human judgment and context, therefore the analysts warn that AI-induced layoffs could be costly in the long-term despite offering potential savings today.

In fact, the study warns that by as soon as 2029, one in three (30%) employees laid off because their jobs were replaced by AI will need to be rehired, and potentially at a higher cost to their future employers than had they just been kept on.

Gartner warns about AI layoffs and the importance of human workers

Ultimately, the analysts warn that treating AI primarily as a headcount-reduction mechanism could weaken future talent pipelines, remove institutional knowledge and expertise, reduce innovation ability, and create expensive recruitment challenges later.

First of the four upcoming trends predicted by the company is the human-AI relationship – companies are being urged to build stronger human-AI collaboration where AI and automation are largely seen as augmentation, not replacement.

It's also about a cultural change, because Gartner says workforces should adapt to AI, not just adopt it. Employees will need to learn continuously, adapt to changing roles and understand how to work effectively alongside AI.

Thirdly, human context, judgment and meaning should be protected, with Gartner increasingly worried that AI could inadvertently strip these away.

Finally, Gartner expects AI investments to be a continuous loop and not a single action – companies that reinvest will likely be the ones to see the best success.

"The competitive advantage will go to the CIOs and business executives who build an AI-shaped organization where AI value compounds by reshaping roles and allowing workflows to cross traditional boundaries, increasing velocity and reducing friction," VP Analyst Tori Paulman wrote.

Being a devoted Gemini user seems to require a peculiar combination of loyalty, patience, and an unusually high tolerance for the word Flash.

That patience is being tested again. Anthropic released Claude Fable 5.1 last week, bringing significant improvements to its model lineup. OpenAI followed with GPT-6 Astra, its new frontier model designed to operate software, tackle complicated multi-step jobs, and work with considerably less human supervision.

Over in the Gemini community, meanwhile, some users are staring at their model picker and wondering when Google is going to join the party. The increasingly nervous focus is Gemini 4 Pro, the presumed next major frontier model from Google, and whether the company will actually release it at all.

Google has not announced that Gemini 4 Pro has been canceled, and the increasingly elaborate theories circulating on Reddit should be treated as exactly that: theories. Still, the mood is revealing. One r/Bard post jokes that Astra's arrival means Gemini 4 Pro will now be canceled while everyone waits for Gemini 4.5 Pro instead, and the thread title predicts Google will decide 4 Pro is no longer competitive enough and return to releasing Flash models.

Awaiting the new Gemini

"I also want to feel the frontier," one Reddit thread begins, with plenty of sympathetic agreement from others. "Google doesn't have enough talent anymore, and most of its compute is reserved for Apple Intelligence and Google Search AI Mode," one user posted in response.

The frustration makes more sense when you look at what Gemini users can see happening elsewhere.

Anthropic calls Fable 5.1 one of its most advanced models for coding and knowledge work, with improvements aimed at long-running tasks and research. The model has a one-million-token context window and 128,000-token maximum output, while Anthropic has also made cache reads dramatically cheaper.

Then there is Astra. OpenAI describes GPT-6 Astra as its most intelligent model yet, with an emphasis on computer use, coding, and completing complex work across multiple steps. The important part for ordinary enthusiasts is that OpenAI says Astra is rolling into ChatGPT, including access through Plus in Work and Codex as rollout progresses.

Gemini users want their turn.

Another Reddit thread is literally titled “I envy them too,” but most of the comments there are pushing back against the idea that Gemini's current model is useless. "Just keep all three subs, best way to play," one post argues.

Still, that misses something about why people become attached to these platforms. Gemini comes with Google's wider ecosystem, Android integration, and tools such as NotebookLM. As one Reddit commenter put it when told to subscribe elsewhere, “Gemini is cooler, has more tools, NotebookLM, it's better integrated in Android, etc.” Switching models is easy. Switching ecosystems is less so.

Gemini Flash again?

There's a running joke is that Google will respond to all this impatience by releasing another Flash model. Google has continued iterating rapidly on its faster Gemini models, and the current Reddit discussion is full of references to Gemini 3.7 Flash and 3.8 Flash.

In the thread speculating about Gemini 4 Pro's fate, one commenter imagines Google announcing, “We have started our most ambitious pre-training run yet, for Gemini 4.5,” while another delivers the punchline: “Here's another few Flash models.” A less amused commenter writes that DeepMind has “lost the initiative” and its famous confidence.

This is not really a fight over whether Gemini 3.8 Flash is good. Some users think it is excellent. Multiple commenters argued that Gemini remains highly competitive on price and performance and wins when it comes to coding.

The feat is that, after years of seeming to have an AI advantage, Google has squandered it. When Gemini looks like it is following OpenAI and Anthropic rather than forcing them to respond, its most enthusiastic users notice.

Of course, whether Google even wants the same victory its Reddit fans want is debatable. A cheaper, faster model embedded across Search, Android, Workspace and countless other services could ultimately be much more valuable than topping a frontier benchmark for a few months. For Google shareholders, that could be wonderfully sensible. For the person refreshing the Gemini model picker while Claude and ChatGPT users play with their new frontier models, it is rather less exciting.

2025 was a transformative year for artificial intelligence, and 2026 is already proving to be equally significant.

While generative AI dominated conversations just a few years ago, the focus is now shifting towards agentic AI, where intelligent systems can take action, interact with business processes, and support employees in more meaningful ways.

As these capabilities continue to evolve, organizations are looking for better ways to connect AI systems to the applications, data and services that drive their operations.

This is where technologies such as Model Context Protocol (MCP) are becoming increasingly important.

Rather than creating bespoke integrations for every tool or system, MCP provides a standardized way for AI models and agents to access information and perform actions across an organization's technology estate.

While MCP is not a requirement for every AI implementation today, it represents a natural next step for organizations looking to move beyond isolated AI use cases and towards more integrated, scalable AI ecosystems.

However, greater integration also introduces greater responsibility. Effective governance remains essential for any AI deployment, but it becomes even more critical when autonomous agents are granted access to business systems, processes and sensitive information.

Organizations must establish clear guardrails that define what agents can access, what actions they can perform, and how their activities are monitored. Without appropriate oversight, businesses risk agents operating beyond their intended scope or creating unintended consequences across interconnected systems.

The impact on Software-as-a-Service

Few sectors will feel the effects of this shift more than Software-as-a-Service (SaaS).

For years, SaaS applications have been built around human interaction. Users access platforms through dashboards and interfaces, navigate predefined workflows, and manually complete tasks. The application itself serves as the primary workspace where work is performed.

Agentic AI introduces a different model.

Rather than navigating software in the same way a person would, agents can interact directly with APIs, services and data sources. This allows them to retrieve information, execute actions and orchestrate processes across multiple systems without relying on traditional user journeys.

That does not mean SaaS applications will disappear. In fact, they will continue to play a critical role in storing structured data, enforcing business rules and managing workflows. What is likely to change is how those applications are consumed.

Instead of being the primary destination where work happens, many SaaS platforms will increasingly act as sources of capability and information that AI agents can utilize on behalf of users. As a result, organizations may find themselves focusing less on which application employees need to access and more on how services and data can be brought together to achieve the desired business outcome.

Human interfaces will still matter. Users will continue to need visibility, exception handling and control mechanisms, particularly when business-critical processes are involved. The challenge for software providers will be balancing traditional user experiences with new AI-driven interaction models while maintaining compatibility, reliability and operational resilience.

Breaking down agent silos

The next stage in the evolution of agentic AI is not simply creating more agents. It is enabling agents to work together effectively.

Many organizations already struggle with fragmented systems, disconnected data and isolated processes. Without careful planning, agents risk creating a new generation of silos, each operating within its own limited context and producing inconsistent outcomes.

To avoid this, businesses must focus on shared context, connected data and interoperable services. The goal is not to have individual agents automating isolated tasks but to enable multiple agents to contribute towards broader business objectives across entire processes.

When agents can access consistent information and operate across organizational boundaries, the value shifts from discrete task automation to coordinated execution. Rather than supporting an individual stage of a workflow, agents can participate in end-to-end processes while remaining aligned to business policies, operational requirements and organizational goals.

This represents an important architectural shift. Software increasingly becomes something that agents consume programmatically, while integration, context and orchestration become central to delivering outcomes at scale.

Mitigating risk in AIaaS

Unlocking these new capabilities requires more than deploying AI tools. Organizations need governance frameworks, security controls and operational processes that allow autonomy to be introduced safely and responsibly.

As agents gain access to more systems and collaborate across workflows, operational complexity inevitably increases. Businesses must define clear policies around what agents can and cannot do, what data they can access, and what approvals are required before actions are taken.

These controls should be embedded into the orchestration layer itself, ensuring governance is applied consistently across all agent-led activities rather than being treated as an afterthought.

Traceability and accountability are equally important. Completing a task successfully is only part of the equation. Organizations must understand how decisions were made, what information was used, and which policies were applied throughout the process. This visibility will be essential for compliance, security and maintaining trust in autonomous systems.

The role of developers will also evolve. Rather than spending significant time building and maintaining point-to-point integrations, they will increasingly focus on designing agent behaviors, defining boundaries, managing orchestration and ensuring solutions operate within established governance frameworks.

The organizations that succeed will be those that balance innovation with control. Too little governance introduces risk, while excessive restrictions can prevent businesses from realizing the benefits of AI altogether.

Agentic AI should not be viewed as a replacement for software development or existing technology investments. Instead, it represents a powerful new interaction layer that changes how organizations access information, automate processes and deliver outcomes.

Businesses that invest now in integration foundations, governance models and workforce skills will be best placed to take advantage of the opportunities this next phase of AI creates.

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Whenever a new AI model is released in a cut-down form because it is too powerful, or one transgresses its sandbox and performs an unexpected cyber hack on a company’s website, we tend to see jokes made about Skynet or HAL 9000 becoming a reality.

But science fiction didn’t just predict our current relationship with AI, it influenced it. Our fascination with artificial life began long before computers — or even electricity — existed. Ancient Greek stories featured mechanical servants created by the god Hephaestus, while Jewish folklore gave us the Golem, an artificial being brought to life to carry out its creator’s commands.

In their new book, Myth-Made Machines: The Stories Behind Artificial Intelligence, authors Dave Bradley and Kelly Vero trace humanity’s dreams and fears about machines that can think, through mythology, literature, movies and television.

Their argument is that these stories haven’t simply anticipated AI; they have shaped how we talk about it, what we expect from it and, in some cases, the technology people have gone on to build.

I spoke to Bradley and Vero about why humanity keeps creating machines in its own image, whether science fiction has influenced today’s AI developers, and why our response to artificial intelligence still swings between two extremes: “If we don't like it, we’ll kill it. If we love it, we’ll marry it.”

The ancient beginnings of AI

TechRadar: Talk me through the title - why is it called Myth-Made Machines?

Dave Bradley: The core argument of the book is that humans have always been fascinated by the idea of creating thinking machines and artificial life. When ChatGPT formally launched in 2022, one of the first jokes I saw on social media was about Skynet coming online. It struck us that the whole way we understand and talk about this new technology is shaped by stories we’ve been telling ourselves for years. Kelly has a deep knowledge and love of Greek and Roman myth, as well as stories such as Frankenstein, while I’m a huge science fiction nerd (and occasional medievalist). Through our conversations, we realised that our cultural idea of “AI” is much older than modern computers.

Kelly Vero: Myth-Made Machines is the essence of where every element of AI or robo-futurism comes from: Myth. With myth, we, as humans, have to navigate the real and the prescient from the propaganda and the fanciful.

TR: When did artificial intelligence first enter our human stories?

KV: When we started researching the book, it was Greek myths that we started with. We found our own Sam Altman, Elon Musk and Dario Amodei hiding in plain sight.

DB: The idea is incredibly ancient. In Greek myth, Hephaestus creates golden workers to help him in his workshop. Jewish legend brings us the Golem. The idea that one might build autonomous servants out of inanimate material is thousands of years old.

For something closer to what we would recognize as a computer, Jonathan Swift’s Gulliver’s Travels gives us a wonderful early example in the 18th century. The Engine of Lagado randomly rearranges words in the hope of generating useful knowledge. Read today, it looks uncannily like a satirical ancestor of generative AI.

The term ‘artificial intelligence’ in the modern sense of machine learning and reasoning was coined in the 1950s by computer scientist John McCarthy — and that’s when it starts creeping into TV and film too. There are some great early Twilight Zone and Star Trek episodes about the possibilities and perils of machines in our homes and workplaces.

TR: Which are your personal favorite sci-fi AIs/robots, or movies and TV shows that involve AI?

KV: I’m a Lawnmower Man aficionado, but having worked on the actual Transformers franchise, I’m also a Decepticon through and through.

DB: I’m a huge sci-fi geek, and to this day I maintain that R2-D2 is the most important character in Star Wars. He has a mission, gets on with it, saves everybody repeatedly and barely receives any credit.

But I also nerd out over Marvin in The Hitchhiker’s Guide to the Galaxy, the Cylons in both versions of Battlestar Galactica, and, of course, Data in Star Trek: The Next Generation, who I’d argue is the character on the real hero’s journey in that series.

Side note: HAL 9000 slightly infuriates me, mainly because people have been quoting “I’m sorry, Dave, I’m afraid I can’t do that” at me personally for decades.

TR: Do you think our myths about conscious machines have influenced the real development of AI?

KV: Absolutely! Human life is all about dreaming up concepts to adore or fear; it’s how we roll.

DB: Today’s engineers and entrepreneurs grew up on Star Wars, Star Trek, Isaac Asimov and William Gibson, and there’s good evidence that science fiction influences the people who go on to build the future. One 2022 study found that 93 per cent of UK astronomers enjoyed science fiction, for instance, and 69 per cent said it had influenced their career decisions.

Sometimes the connection is very direct. Pete Bonasso at TRACLabs has credited HAL 9000 from 2001: A Space Odyssey as an inspiration for his work on NASA space AI systems. Asimov’s Three Laws of Robotics were invented for fiction, but they’ve become a reference point for real-world thinking about AI safety. Google DeepMind’s 2024 ‘Robot Constitution’, for example, sets out safety rules for machines in a way that is very much in that tradition.

TR: What do you think of the current crop of AI models?

DB: I follow this area as closely as I can, although you have to bear in mind that it changes almost daily. We’ve just seen another generation of frontier models arrive. It moves fast, which is fascinating for tech writers, although it also explains why we wrote a book largely about things that happened hundreds of years ago rather than trying to capture the state of AI in 2026!

When it comes to video games — still my day-to-day working life — I believe it’s going to be important to work out what new experiences AI can bring to the table, not just how quickly they can automate existing processes.

KV: They’re facsimiles of every book, TV series, movie or game we’ve ever imbibed. They’re not as clever as we are and they never will be.

TR: Do you think of AI as a threat to humanity, or as an opportunity?

KV: A super opportunity to ride alongside a civilization’s worth of knowledge? It’s almost too good to be true. If you think about it, until AI came along, humans held a monopoly on intelligence. We kind of only knew what we could discover or search for as humans (history is always written by the victor). Today, we can effectively outsource our thirst for knowledge to something else and tell it how we should understand the results; for the most part, we get balance: the rest is sponsored content!

DB: It feels glib simply to say ‘both’, although I’m tempted. Personally, I’m an optimist by nature, and I like to believe the general direction of travel is towards progress, growth and better lives. That doesn’t mean everybody prospers equally, or that there aren’t important battles over jobs, ownership, ethics and power along the way. William Gibson famously said that the future is already here; it’s just not evenly distributed. I think that’s a useful way to think about AI too. The interesting question isn’t simply whether the technology is good or bad, but who prospers from it, who controls it, and how widely any benefits (and risks) are shared.

TR: Do you think we’ll ever reach man-made super intelligence, and if we do what happens then?

KV: We have man-made everything else, don’t we? Why should this be different? For me, the detail lies in sentience. We already have man-made superintelligence, but we don’t have fully operational sentience yet. When something can blink its eye without thinking as quickly as humans can, well, let’s talk again.

DB: I don’t think it’s going to emerge simply by scaling today’s transformer models, regardless of some of the more excitable predictions around them. However clever and interesting LLMs are, these are fundamentally probabilistic systems that generate text, images and other media. That doesn’t make them conscious.

An omnipotent silicon supervillain, like Skynet, makes for good screen drama — but if we do eventually create genuine superintelligence by some route, I’d actually hope that a networked brain with access to vast amounts of information, capable of seeing problems from many different perspectives, would naturally tend towards connection and assistance rather than destruction. But I’m not naïve about it. Systems also magnify our biases, flaws and prejudices. That’s why alignment matters.

TR: What have you learned about humanity's approach to AI from writing this book?

KV: That we’re the only stakeholders. We’re the arbiters of our own creation(s); if we don't like it, we’ll kill it. If we love it, we’ll marry it. AI is all of us, and my main worry is that only 6% of the world’s population currently decides on how the other 94% will live, work and adapt to an AI future. However, if the past is to be believed, we’re probably going to be ok. (Famous last words, I know!)

DB: Technology and automation are things humans do. What became very apparent is that humans repeatedly try to create things in their own image, while simultaneously struggling with the implications of that. We worry about job losses, replacement and loss of identity. We’re fascinated by creating something powerful, then immediately frightened that it might overthrow us. But the fear of the servant who will turn on us, or the untrustworthy ally who might sneakily replace us, is not limited to machines, right? We might project those stories onto computers in our books and films, but the subtext is often about human paranoia around class, politics, war…

More specifically, there’s a recurring oscillation between loving and fearing our own technology. In the 1970s, amid Cold War paranoia, computers were frequently instruments of doom: Colossus, Cylons, HAL 9000, even V’Ger in Star Trek: The Motion Picture. In the 1980s, as PCs and games consoles became familiar household playthings, popular culture increasingly gave us machines as loyal companions: KITT, Metal Mickey, Twiki, Nono and Data. Then the pendulum swings back: by the late 1980s and 1990s, we get The Terminator and The Matrix. You can trace those alternating waves of optimism and anxiety all the way back through the Industrial Revolution. The technology changes, but our emotional responses to it are remarkably consistent.

Author bios and book details

Myth-Made Machines: The Stories Behind Artificial Intelligence

co-written by Dave Bradley and Kelly Vero

Published by Bloomsbury on 3rd September 2026

Available as ebook or hardcover

• AI data centers currently use a huge amount of energy
• Scientists have found a way to cut that energy bill by "orders of magnitude"
• It involves using "ultrafast magnetic-field pulses" in RAM and storage

It’s no secret that the current artificial intelligence (AI) boom is leading to record levels of energy consumption — all that computing power needs to be fueled somehow — and it’s causing much controversy among the communities that are impacted whenever a new data center lays down its roots in their neighborhoods.

Now, though, scientists think they’ve come across a way that could radically slash the energy requirements of memory and storage, with potentially massive consequences for the future of the computing and AI industries.

In a paper published in the Advanced Materials journal (via a press release in Science Daily), researchers at the University of Edinburgh in Scotland wrote that the breakthrough would fundamentally affect the way magnetic memory operates. Right now, switching its state allows magnetic memory to control digital information, but the current methods of doing so can be costly in terms of energy.

Instead of using standard energy switching techniques, the researchers discovered an alternative that harnessed ultrafast magnetic field pulses that consumed far less energy. In fact, the paper claimed that the authors’ approach could cut energy usage by “up to two orders of magnitude” — in other words, a cut of around 100x, which is a significant reduction.

The data centers of the future

The AI revolution is only a few years old, but data center energy consumption has already become a significant issue. As the authors of the research paper put it, “Without significant improvements in efficiency, [information and communication technologies] could eventually represent a sizable share of worldwide electricity consumption and carbon emissions.” AI is playing a large role in that trend thanks to both its voracious appetite for component production and the sizable emissions it produces.

While the paper focused on magnetic memory, its theories could be applied to other fields, the authors believe. As study writer Dr. Elton J.G. Santos put it, “The same framework can be adapted to electrical currents and even ultrafast laser pulses, which are among the most cutting-edge technologies for future data storage. That means the ideas developed here could have applications far beyond the systems we studied.”

Although the research is still in the realm of theory, the paper’s authors proposed a number of practical steps that could help others build prototypes and conduct experiments.

That said, don’t expect monumental changes any time soon. There is still a long road ahead before these ideas get put into practice, if they ever do. But given the promising results of the scientists’ work, there is hope that the data centers of the future could be far less energy-intensive than those of today.

Most governments still talk about artificial intelligence as something to regulate or subsidize. South Korea is trying something considerably bolder. It wants to make advanced AI available free to every citizen, treating access less like a premium software subscription and more like a basic piece of national infrastructure.

The country's Ministry of Science and ICT has selected three consortiums, led by SK Telecom, KT and Kakao, for its "AI for All" program. The plan is to give Koreans free, unlimited access to general-purpose AI services built primarily on domestic models, with beta testing before a full launch expected by the end of 2026. The government is supplying a combined 512 Nvidia B200 GPUs this year and plans to subsidize nationwide operating costs from 2027.

That already sounds like a government-backed ChatGPT competitor, but South Korea's ambitions go considerably further. The services are supposed to develop into agents capable of helping citizens find government programs, submit applications, make reservations and payments, and carry out other tasks. The government's longer-term ambition is effectively one AI agent for every citizen.

Plenty here could attract international attention, but the project’s real value may have little to do with giving everyone a free chatbot.

Far more than a chatbot

South Korea has a strong argument for intervening. Its science ministry says roughly two-thirds of Koreans have used AI services, and around 23 million people have used generative AI. ChatGPT alone had an estimated 23.45 million monthly active users, with Gemini and Claude having far lower but still notable monthly users of 8.45 million and 2.41 million, respectively.

Those figures are impressive until you consider the people missing from them. If AI really does become as important as its advocates believe, the ability to pay $20 or $30 every month for the best models starts to matter. Free tiers help, but their limits, features, and even continued existence are decisions made by private companies.

South Korea's government has explicitly identified that dependence as a problem. It argues that citizens relying on free versions of foreign AI services remain vulnerable to usage restrictions and price hikes. Its answer is a publicly supported alternative built largely around Korean models, with domestic models required to account for at least half of the service. Plus, South Korea gets to widen AI access while supporting domestic model developers and reducing its reliance on foreign platforms.

“South Korea’s ‘AI for Everyone’ project rests on a principle worth defending: access to AI should not depend on what you can afford, and a country is stronger for having its own capability. The ambition is not in question. How it works in practice is,” said Nik Kairinos, CEO & Co-founder of RAIDS AI, which provides testing and detection of threats to AI services.

“When a publicly provided model or agent goes rogue, gives someone wrong information, or causes harm, who answers for it? The government that commissioned the service, the company that built it, or both? Citizens will not have chosen their provider, and many will not know which one they are using.”

Combining AI with public services

I am skeptical about the idea of governments offering general-purpose AI subscriptions for basic technical reasons, as well. AI models improve at an absurd pace, and taxpayers would effectively be underwriting an endless technological race against OpenAI, Google, Anthropic, and whatever comes next.

“I think it is not rational to spend money on an unlimited general chatbot striving to compete with ChatGPT, Claude, or Gemini, since the commercial world of artificial intelligence changes very quickly,” said John Park, a founding membеr of technical staff at agentic AI startup AGI, Inc.

“Instead of that, a more appropriate strategy will be to combine AI with public services that people are already using, for instance, with the government’s call center, benefit applications, tax assistance, appointment booking, and document creation. That way, AI becomes part of the cost of providing public services, just like a website or a call center.”

That, to me, is the real blueprint hidden inside South Korea's experiment. Governments don't need to build the world's best chatbot. They need to make dealing with government better. An AI that knows which parental benefit you qualify for, finds the correct form, and explains any baffling sections before helping you submit it is useful in a way that justifies public spending. A do-anything chatbot lacks that justification

That's the strategy in South Korea. Kakao plans to put its service inside KakaoTalk. While KT has presented an agent spanning public services, education, finance and shopping, and SK Telecom plans phone- and text-based agents for those reluctant to engage with apps or websites.

You can see hints of similar ideas in other countries. The UK launched GOV.UK Chat in its government app in May, allowing people to ask natural-language questions about government information. Singapore's VICA conversational AI platform is used across more than 60 government agencies, and France announced plans for a public health chatbot and a conversational assistant. AI as a public utility probably makes sense when the "utility" is access to government itself.

Privacy policy for freedom

The difficulty begins when an assistant stops pointing citizens toward services and starts acting on their behalf. A chatbot incorrectly describing a benefit is bad. An agent incorrectly applying for one, canceling something, making a payment, or supplying inaccurate information under your identity is an entirely different class of failure.

“Once an agent is transacting for a citizen inside public services, the bar for knowing what it did, and why, is far higher than for a system that only answers questions,” Kairinos pointed out. “Other governments will be watching this closely, because most of them will face the same choice within a few years. Widening access to AI is worth doing. It just cannot come at the expense of accountability, evidence and public trust.”

That warning becomes particularly important when considering what people actually tell AI assistants. We already happily type things into commercial chatbots that we might hesitate to tell acquaintances. Put an AI assistant between a citizen and the welfare system, tax authority, or health service, and the information becomes far more sensitive.

“Citizens utilizing AI for benefit applications and other services could disclose very personal details about themselves: health, income, family, and finances. Such information needs to be kept separate from other aspects like advertising, commercial profiling, and training of models,” Park said. “Citizens need to have access to such essential services without having to consent to the use of their data elsewhere. Otherwise, the free service may cost the citizenry nothing in monetary terms, but at the cost of their personal information.”

There is an uncomfortable detail in South Korea's own description of "AI for All" here. The science ministry has said participating companies will be expected to develop revenue models, giving the example of using prompt data collected while providing the services. That may help make free AI financially sustainable, but it also makes the boundaries around data use enormously important.

Citizens should not have to wonder whether describing their unemployment, disability, debt or family circumstances to obtain public assistance is also enriching a commercial profile or future training set. If AI becomes part of the machinery of government, privacy protections need to resemble those surrounding public records more than the terms and conditions of a consumer app.

Public utility, not public ChatGPT

South Korea may simultaneously prove that universal AI access is a good idea and that "free ChatGPT for everyone" is the wrong interpretation of it.

“South Korea is a successful example of how a nation can increase AI adoption, help local firms, provide better language-specific solutions, and decrease reliance on international websites. The U.S. would most likely develop a more fragmented system of AI services with different programs dedicated to tax, benefits, healthcare, education, and library applications,” Park said.

“Basic capabilities of AI, like search, translation, summarization, form filling, and help with public services, could turn out to be free or almost free. The main task of the government will be to ensure accessibility, privacy protection, transparency, and the right to human review, while private companies continue competing on more advanced services.”

That fragmented version is ultimately more convincing than the grander vision. There is an extremely strong argument for ensuring nobody is denied access to healthcare, benefits, or tax assistance because they cannot afford the AI tools increasingly used to navigate them.

Governments can guarantee a baseline of useful capabilities tied to citizenship and public services. Private companies can continue fighting ferociously over the frontier, selling better models, enormous context windows, creative tools, and more.

South Korea is attempting something much larger. It wants broad AI access, domestic technological sovereignty and eventually personal agents that can conduct parts of citizens' economic and social lives. That makes the country a very expensive live experiment rather than a blueprint. AI is becoming part of how the country thinks about economic infrastructure and public administration.

Other governments should copy the principle before they copy the product. Access to basic AI capabilities is likely to become important enough that leaving it entirely to monthly subscriptions will create another digital divide. Governments can ensure everyone benefits from the real value of AI tools, which will probably be the most boring examples and come with strict data rules.

If South Korea's plans work out, countries around the world will have a template for making AI a basic digital service. Any failure will prove just as valuable as a lesson.

I have developed a fairly strong allergy to the AI slop filling some corners of the internet. It's not that I haven't seen impressive demonstrations of what AI models can do visually; it's just that the baseline creations made by most people are execrable at best and blandly monotonous when they aren't horrifying or malevolent.

ChatGPT’s new Stickers feature didn't immediately strike me as a chance to change my opinion, but the option to import the results into iMessage and WhatsApp was interesting enough to make it worth experimenting with. I went with a photo of my chihuahua and asked for a few options of stickers based on her. While I am obviously biased about her, the results actually looked like images I would not be ashamed to use in a conversation with someone.

Sticker emotions

ChatGPT’s Stickers tool is in the platform's Image section only on the ChatGPT mobile app. Open the main + menu, pick Images, then click Stickers, which is currently the first option on the long list of templates. ChatGPT has a prompt ready to go, but you can adjust it for different styles or add different emojis to the design. Once the results are ready, the “Add to chat apps” button appears, and ChatGPT can send them directly to your WhatsApp or iMessage sticker library.

The source photograph makes a noticeable difference. Choose one with the animal or person clearly separated from the background, good light on the face, and important features fully visible. A dark dog against a dark couch did not work well. And be sure to mention any details you want to make sure are highlighted in the prompt.

At first I asked for photographic stickers to see how well it did at consistency. While not perfect, the best stickers preserved the details that made my dog recognizable. Her expressions became exaggerated, but she remained the same dog. Even when I wrote some of the prompts for new stickers from scratch, ChatGPT managed to keep my dog looking like a (sillier) version of herself.

Cartoon canine

After testing the more realistic stickers, I tried pushing the feature toward its cartoonier options. The chibi-esque result is fun in its own way. The resemblance became looser, particularly around her coloring and age, but the emotional range improved with the flexibility.

Just specify one illustration style and list any specific reactions you want.ChatGPT may still reinterpret a few physical details, but consistency matters more than perfect accuracy once your dog is wearing sunglasses and wondering whether she is cool yet.

AI slop is still bad, and the way it buries useful information beneath a mountain of disposable and often problematic images is an issue that has to be addressed. But when used for something like personalized WhatsApp stickers, AI tools can be attractive. The best use of generative AI images may be smaller and more personal, not a content factory, but a way to add a little fun to online chat.

Sam Altman does not like smart glasses. Speaking on the Sources Podcast, the OpenAI CEO said he finds it “very uncomfortable talking to people with a camera and a light” and confirmed that he does not wear smart glasses himself. That is a surprisingly firm position considering where the technology industry appears to be heading and the success of products like Meta’s Ray-Ban glasses.

Then again, Altman is working with former Apple design chief Jony Ive on an entire family of AI devices. OpenAI has kept the Ive project deliberately foggy, but Altman has suggested they are making a range of AI hardware. His comments about smart glasses draw one clear boundary, removing the idea of a camera pointed at everyone as a feature.

Smart glasses have visible drawback

Altman does identify a fundamental flaw in smart glasses that has little to do with processors, battery life or AI model choices. Glasses place technology directly between two people looking at each other, and adding a camera turns an ordinary conversation into one where the other person has to wonder whether they are also an unwilling piece of content.

Smartphones established a useful social signal around recording. Someone pointing a phone camera at you is usually fairly conspicuous. Camera glasses erase much of that distinction because their great design achievement is making a recording device look inconspicuous. It's why similar tools have been a staple of James Bond-style spy fiction for decades.

Australia’s eSafety commissioner has already asked manufacturers to make recording indicators impossible to disable and automatically blur faces when informed consent is absent. The regulator cited women being covertly filmed for social content and rejected the idea that telling customers to behave responsibly amounts to a safety policy. The backlash reflects a public tired of paying the social price for somebody else’s convenience.

Important exceptions to the argument against camera glasses exist. The same visual awareness that creates privacy problems can be genuinely transformative for blind and low-vision users, something Australia’s regulator explicitly acknowledged. That makes an outright rejection of the technology too simplistic, but it also strengthens the case that camera glasses may be better suited to particular needs rather than as the default AI hardware.

Wearable, not watchable

Altman’s comments about AI hardware point to an all-encompassing strategy of making devices for tables, pockets, and "on your body,” and that OpenAI will take time to launch all of them. OpenAI has previously described its work with Ive as an attempt to move beyond traditional interfaces. So not just a miniature smartphone strapped to your face.

An audio-first wearable suddenly makes far more sense. An earpiece, pendant, clip or other discreet device could provide persistent access to an AI assistant without placing a camera directly in the sightline of everyone the wearer encounters. It could listen when summoned and respond quietly like wireless earbuds or smartwatches.

Some may prefer the camera and the data an AI assistant gains from it about the physical world. And of course, putting microphones on wearables raises privacy questions just fine without cameras. Making the device unobtrusive yet observant is the trick Ive and Altman have to perform lest it face the same social opprobrium as the AI smart glasses.

Ive built his reputation around products whose complexity receded behind their physical design. But AI assistants will know and hear far more than an iPod ever did. A successful AI wearable cannot merely make advanced computing invisible to its owner. It must make its intentions legible to everyone else.

Meta is betting that the camera can become socially acceptable because they are built into discreet smart glasses; OpenAI may instead bet on a successful AI wearable that won't potentially creep out everyone you pass on the street. The best OpenAI wearable feature may in fact be an absence, and "no camera" its most enticing selling point.

Organizations are investing heavily in artificial intelligence, but technology alone isn’t going to determine success. The businesses that unlock AI's full potential will be those that give governance the same attention as innovation.

In the rush to deploy AI tools, too many organizations are treating governance as something that can be addressed later. AI introduces a level of speed, autonomy and organizational impact that traditional governance frameworks were never designed to manage. Businesses that fail to rethink governance now risk increasing operational risk, slowing future adoption and undermining the value of their investments.

Organizations need to shift their mindset and recognize that implementing AI requires fundamental transformation in how decisions are made. With governance, accountability and transparency to be embedded from the outset, not pulled together after deployment.

AI is transforming organizations, not just technology

Unlike previous technology transformations, AI is influencing how decisions are made across almost every part of the business. From customer service and finance to human resources and operations, AI is becoming embedded in day-to-day processes and increasingly making recommendations, or decisions, with minimal human intervention.

As AI becomes more deeply integrated into business operations, responsibility can no longer sit solely with technology teams. Legal, privacy, risk, compliance and business leaders all have a role to play in ensuring AI is implemented responsibly and consistently.

Successful AI adoption is therefore as much an organizational and cultural shift as it is a technology transformation. Businesses that continue to treat AI as a standalone IT initiative risk fragmented ownership, inconsistent governance and missed opportunities to scale AI effectively.

Traditional governance wasn't built for AI

Many organizations still operate with a "deploy first, govern later" mindset, believing governance can be introduced once AI is established. The reality is that governance becomes significantly harder once AI is embedded across business processes.

Traditional governance models were built for a much slower pace of technology adoption. AI changes that equation. New tools, models and autonomous agents can be introduced into workflows in hours, while governance often still relies on manual reviews, siloed assessments and reactive oversight.

This creates a widening gap between AI innovation and organizational readiness. At the same time, AI is creating entirely new data use cases, while regulatory expectations continue to evolve. AI's use of data can also be dynamic and unpredictable, meaning documented controls are no longer sufficient when outcomes can't be predetermined.

Organizations need AI-ready governance – governance that evolves alongside AI through automated, collaborative assessments, programmatic controls embedded at the data layer, and continuous monitoring of risk across the business. As AI operates 24/7, governance must provide ongoing visibility into an organization's risk posture, rather than relying on point-in-time reviews.

The result is a growing disconnect between AI adoption and governance maturity. While organizations are investing heavily in AI, many have yet to modernize the governance, operating models and cross-functional accountability needed to support it. Closing this gap requires governance that operates at AI speed, enabling organizations to innovate with confidence while managing risk and maintaining trust.

Building the right foundations

One of the biggest misconceptions about AI governance is that it slows innovation. In reality, strong governance is what gives organizations the confidence to innovate, enabling them to deploy, scale and adapt AI responsibly.

The real shift isn’t purely technological or organization, it’s the combination of both. While AI is driving business transformation, organizations also need the right technical foundations to support it. AI can no longer be viewed as a technology initiative owned solely by IT.

Successfully scaling AI requires business leaders, risk teams and IT to work together, with IT embedding programmatic guardrails and controls that reflect business requirements. Without this partnership, organizational governance alone won't be able to keep pace with the speed and complexity of AI.

The organizations that gain the greatest competitive advantage won't necessarily be those that adopt AI first. They'll be the ones that recognize AI success depends as much on leadership, governance and organizational readiness as it does on the technology itself.

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• Some Chinese models are said to be 90% cheaper than their US counterparts
• Open-weight models are also better suited to local deployment
• Price- and quality-driven markets exist within the AI sector

A new report from Juniper Research has revealed how rapid improvements and dramatically lower operating costs for Chinese AI models could be impacting the uptake of counterpart US models.

The paper details how Chinese AI models can now cost as much as 90% less to run than leading US alternatives, which is already affecting where developers choose to run their AI workloads.

Looking ahead, the battle is becoming less about who can build the highest-performance, lowest-cost models and more about who can make inference cheaper.

Chinese AI models are seriously undercutting US models

Juniper's research focuses on API platform and marketplace OpenRouter, which last year saw around 70% of work being conducted with US AI models from the likes of OpenAI, Google and Anthropic. Their combined share has since fallen drastically to around 30%, the paper notes, noting the clear willingness to move workloads away from US firms to cheaper alternatives.

More broadly, Juniper says two distinct markets have started to emerge within AI spend – a price-driven one where customers prioritize cheaper inference and a quality-driven one where customers are willing to pay more for higher accuracy and reasoning.

While US companies still appear to dominate the quality-driven market, emerging Chinese models deliver better on the price-driven front.

Juniper also noted that open-weight models are challenging the idea that inference needs to be sent a cloud provider. Now that many small models can be deployed locally, cloud providers could also be set to lose out on the major revenue they've been experiencing in recent years.

While what comes next is unclear, it's likely that a growing market could lead to a space where cheaper Chinese models and higher-performance US models could coexist, but one thing's for certain. US dominance is certainly being challenged.

• Fitbit users are complaining about Google Health’s food tracking
• The AI tool has reportedly been logging inaccurate metrics
• One user described the mistakes as ‘almost dangerous’

If you’ve used any kind of artificial intelligence (AI) tool, chances are you might have seen it hallucinate wildly inaccurate answers to your simple questions. Most of the time the results are largely harmless, but what about when these problems start having the potential to affect your health and wellbeing? That’s what many Fitbit users have recently noticed, and it could have serious consequences if it carries on unchecked.

The issue centers on Google Health, an AI helper that has been integrated into many of the best Fitbit wearables. It’s designed to understand your health metrics and make helpful suggestions to improve your health. Unfortunately, many Fitbit users have noticed it is doing the exact opposite when it comes to logging their nutritional intake.

Posting on Reddit, for example, one user said that Google Health’s food logging was “so inaccurate it’s almost dangerous.” Describing their problem, they outlined how Google Health drastically overestimated the number of calories in a serving of eggs while simultaneously underreporting the calorific intake of a portion of Greek yogurt. “On what planet is this level of deviation acceptable for a health product?,” they asked, before adding: “To make matters worse, digging into the food database it is full of incorrect nutrition facts.”

They’re not alone in their irritation, as the Reddit thread is full of similarly frustrated Google Health users. “This is a neverending fight,” said one who lamented that the AI often neglects to add foods when asked, then does so inaccurately when it finally decides to log them.

Another frustrated user has given up on Google’s AI entirely, reporting that: “I have been trying SO HARD to give this the benefit of the doubt, but the hassle of getting nutrition right in the app finally broke me … It also randomly changes my workouts, imported data from other systems, etc. I’ve honestly never been more disappointed in a Google product in my life.”

Another Google Health hallucination

Unfortunately, this is far from the only problem we’ve seen reported when it comes to Google Health. The switch from Fitbit’s previous health-tracking app to an AI-native alternative in the form of Google Health has brought with it a slew of complaints in a range of different areas.

That includes the AI giving users ‘unhinged’ fitness advice, telling ‘nonstop lies’ about their workout sessions, and simply being ‘unbelievably bad.’ The feature has received so much heat that even Google appears to have acknowledged its flaws, with a recent update allowing you to hide the AI tool completely.

It’s a serious reminder of the problems that can arise when an AI tool starts working in potentially damaging areas like health and nutrition. While the original poster’s example of eggs and yogurt may just result in some annoyingly inaccurate metrics, the problems could be a lot worse if their health depended on specific consumption levels or if allergens were involved.

If you have any kind of risk factors when it comes to food and nutrition, it might be a good idea to treat your Fitbit’s Google Health AI with a strong dose of skepticism — or to avoid it altogether for now.

• Michael Kratsios advocates for lighter AI regulation via the Carolina Principles
• Under the system, existing laws would be applied before new laws are created
• China has reportedly signed up despite the scheme aligning with US interests

Speaking at a recent G20 meeting, US officials have urged other governments to rely on existing legal frameworks wherever possible rather than treating AI as a totally new policy problem in a bid not to lose competitiveness.

US technology advisor Michael Kratsios said countries signing up to the proposed Carolina Principles would be in agreement not to write entirely new regulatory systems specifically for AI.

The Principles would only support new rules where AI creates genuinely "novel" situations where existing laws can't be applied.

Carolina Principles push for lighter AI regulation

"Policymakers do not ​need to approach each innovation in isolation and should not treat every emerging technology as a first-of-its-kind policy problem," Kratsios said.

According to Reuters, China has already signed up to the Principles. While the country has not yet confirmed whether this is true, Kratsios did describe a meeting with Chinese science and technology minister Yin Hejun as "great."

Speaking at the event, SpaceX CEO Elon Musk called out current European regulation for harming technological progress. He also touched on the power needs of AI data centers as the sector continues to grow.

Despite the general consensus that lighter regulation could prevent setbacks, Google DeepMind CEO Demis Hassabis urged G20 governments to establish safety testing for AI systems, implying that at least some central oversight could be beneficial for the greater good.

This regulatory debate, which continues to play out, is also closely connected to ongoing US-China tensions, despite the apparent collaboration with China. It forms part of a geopolitical strategy that could keep US companies competitive. The position also closely matches the interests of major American AI companies, which would likely favor lighter regulation and fewer restrictions.

Reuters also reports that OpenAI CEO Sam Altman, Nvidia CEO Jensen Huang and Anthropic co-founder Tom Brown are also set to address delegates as G20 meetings continue.

• Just 4% of service firms have laid off workers in the past six months due to AI
• "Existing workers are much more likely to be retrained than replaced"
• Some companies are still hiring fewer employees than they would have

Just a few years ago, multiple companies every day were announcing AI-related layoffs amid purported efficiency gains. And that came straight after years of post-pandemic layoffs driven by earlier overhiring.

The latest data finally implies that AI-related layoffs aren't so common anymore as companies get to grips with AI and hire new workers to help them find a direction.

According to recent New York Fed data, only 4% of AI-using service firms reported laying off workers as a result of AI in the past six months, with no manufacturers reporting AI-related layoffs in all of 2025 and 2026.

Are companies finally hiring because of AI?

On the flip side, around 13% of service firms said AI had caused them to hire more employees. Those extra recruitments are often needed to help companies deploy and make use of AI.

What the data also shows is that workers aren't being displaced by AI as much as earlier fears implied. More than one in three AI-using service firms are retaining workers, and more than one in five manufacturers are doing the same. They're teaching workers how to benefit from AI's productivity impacts as roles evolve.

"Existing workers are much more likely to be retrained than replaced by AI," the report's authors wrote.

However, despite some positive signs there's still evidence of AI's continued impact on recruitment. For example, 15% of service firms said AI was causing them to hire fewer people than they otherwise would have, up from 12% in 2025.

While it doesn't dispel concerns of layoffs, what this New York Fed data does do is challenge the notion of mass layoffs in favor of a more multifaceted rebalancing, which comprises both hiring, firing and retraining.

A group of developers reportedly lost their jobs during what their employer called an “AI Transformation.” Their response was unusually literal. They built OpenExecutive, an open-source system designed to perform work associated with a CEO and the rest of the executive suite.

The employer and CEO have not been named in the online discussion, and OpenExecutive’s own materials don't present it as revenge for a particular firing. That said, it's hard not to see some specific grievances in both the project and the response online, with lots of comments gleefully welcoming senior management to the automation discussion.

The appeal is easy to understand after several years of executives explaining that AI will make organizations leaner, usually while standing a comfortable distance from the part being trimmed.

OpenExecutive presents one AI executive to the user, but that voice is backed by eight specialist agents. The virtual management team includes equivalents of everything from a chief strategy officer and chief operating officer to general counsel and a board communications director. It can draw on company documents, remember previous decisions, and surface follow-up actions later.

Org chart forked

OpenExecutive uses Anthropic’s Claude models by default, with an executive agent routing requests to whichever specialists seem relevant. The developers pitch it as a C-suite group chat in which everyone responds promptly. The project is distributed under the permissive Apache 2.0 license. Anyone can inspect it, modify it, run it internally or build a commercial product around it.

That open-source element gives the project more weight than a chatbot sold as a digital chief executive might usually represent. It also allows workers to experiment with the same logic their employers have been applying to them. If a company can divide software development into tasks, feed those tasks to models, and keep a few people around for supervision, it can do so.

The comparison is not entirely frivolous. Executive work involves processing information, preparing scenarios, comparing trade-offs, and producing documents for other executives. Those are all activities developers think AI models can handle. It makes OpenExecutive both a product and a satire of AI products.

AI replacement has often been discussed as something that moves upward from the bottom of an organization, consuming routine work before eventually reaching management. The developers have turned the telescope around and discovered that a surprising amount of executive labor also looks like information moving between boxes.

Human accountability

OpenExecutive is still an executive adviser, not a legal chief executive. It cannot accept responsibility or sign documents on behalf of a company. A human must decide whether to act on its recommendations and remain accountable if those recommendations fail.

That's where human leadership can't really be replaced. Analysis is part of the job, but it also involves making choices under uncertainty and accepting responsibility for the consequences. A model can recommend closing an office or cutting a team without feeling the weight of the people affected, while the humans deploying it may find the machine’s apparent neutrality rather convenient.

The project therefore reveals one of the stranger assumptions behind AI-driven layoffs. Companies often judge workers by how much of their output software can reproduce. Executives are more often judged on relationships and judgment.

AI does not erase that distinction, but it makes the special exemption harder to defend. If a developer must prove that their work contains something beyond the code an AI can generate, senior leaders should be expected to prove that their value exceeds AI recommendations. A large salary should buy more than the ability to schedule another meeting about efficiency.

Recent corporate experiments also suggest that replacing people can be easier to announce than to sustain. For instance, Duolingo’s CEO softened an “AI-first” memo after backlash over plans to phase out contractors where automation could handle their work.

Automation keeps happening, but there should be more questioning of its benefits. AI can help a small group start a company without immediately assembling a miniature court of consultants and executives. It can also give existing leaders a new justification for concentrating control while reducing the workforce beneath them.

OpenExecutive will not make CEOs obsolete, at least today. Its recommendations require scrutiny and auditing. But it shows the rhetoric of AI efficiency goes both ways in corporations. Once leaders decide that every job should be broken into tasks and measured against a machine, their own work becomes fair material for the same experiment. The resulting AI CEO may never fire its human counterpart, but it has already made the performance review considerably more awkward.

• AI-shaped vocabulary is becoming harder to separate from ordinary human writing
• The analysis found a sevenfold rise in AI-associated vocabulary use
• Rewritten AI text can enter published research under human authorship

Fırat Mıhcı, a computational linguist and founder of HumanizeMy.ai, argues that AI can correct its own writing style until common detection signals disappear entirely.

The analysis claims that a second AI model can rewrite an original AI draft until identifying traits become far less visible to reviewers.

Once a human editor approves that rewritten draft, it gets published under a real name instead of being labelled as machine-generated.

A study of nearly 19,000 abstracts

The study behind this claim examined 18,989 abstracts spanning three separate academic fields, published across the years 2019 through 2026.

The analysis compares word frequency trends before and after the release of widely used AI writing tools in late 2022.

In computational linguistics, AI-associated word use rose from 1.9 to 14.0 instances per 10,000 words after late 2022, roughly a seven-times increase.

Neuroscience abstracts showed a similar pattern, climbing from 1.7 to 8.7 instances per 10,000 words, close to a five-times increase.

Mathematics abstracts barely changed, moving only from 0.9 to 1.3 instances, which researchers treated as a control group for comparison.

The study noted that overlapping confidence ranges in mathematics made the small increase statistically indistinguishable from no change at all over the full study period.

One tracked term, "delve," showed up in 2.75% of surveyed abstracts during 2024 before falling to just 0.12% within an incomplete 2026 data set.

A feedback loop that confuses detectors

Mıhcı shared the findings as an open study meant to expose a blind spot in current AI detection methods used across many academic publishers today.

According to the study, this pattern creates a cycle in which AI wording gradually becomes accepted as ordinary human vocabulary.

Detection tools trained on older writing samples risk falsely flagging real authors whose natural vocabulary now resembles AI-generated text more closely than before.

AI tools trained on newer papers instead risk absorbing AI-influenced writing as a normal baseline, making future AI text harder to catch.

Words such as "intricate," "showcase," and "nuanced" were also tracked, alongside "underscore," a term commonly linked to AI-generated phrasing.

Editors and publishers reviewing these abstracts reportedly did not indicate that the wording carried any artificial origin before the manuscripts were accepted for publication.

"AI only needs its rewritten output to be accepted once as human. After that, the disguise becomes part of the answer key," said Fırat Mıhcı

The research does not claim every abstract containing these words was written by artificial intelligence rather than a human author.

It instead argues that AI-associated language has entered published writing in ways that make word-based detection increasingly unreliable over time.

Because the 2026 sample remains partial, the reported decline in words like "delve" may still shift once more data arrives.

If AI-shaped phrasing keeps entering the published record undetected, the very baseline used to define human writing may keep moving.

This pattern raises significant questions for any field relying on writing style alone to separate human output from machine output.

You can read about the research here.

ChatGPT Work’s new event-triggered scheduled tasks offer an intriguing alternative to the compulsive email checking that fills my days. The feature essentially acts as an AI-powered secretary, screening your Gmail inbox, Slack channels, and other communication channels, then alerting you or responding as you request in a prompt.

I decided to use the feature as an aggressively selective email assistant. ChatGPT could watch everything arriving in my inbox, but it was only allowed to interrupt me when a message created an actual obligation, and it should suggest how to respond and have a draft ready to go.

Event-triggered tasks are available on eligible plans in ChatGPT on the web and mobile. I used ChatGPT on the web and connected my Gmail account when prompted. You can see the prompt I used below.

The current event triggers support new Gmail messages, which can optionally be narrowed by sender or subject. I wanted ChatGPT to judge every incoming message by its contents, so I left the trigger broad and made the instructions selective.

That distinction matters. The trigger decides when ChatGPT wakes up, while the prompt decides what it does once awake. In my case, every new email could prompt a run, but only messages meeting my criteria were supposed to produce an alert.

I closed my inbox and waited to see how the afternoon went.

Here's how to do it

My original prompt was fairly basic. I told ChatGPT, "When I receive an email that requires me to do something, or comes from a family member, tell me what I need to do and draft a potential reply."

It still left plenty of room for interpretation, and I was wary of spam getting listed as something I needed to do something about, but it came down to being a good test for ChatGPT.

After a bit, I did clarify to ChatGPT that it should alert me about any email with a single sentence explaining who was writing and what they might need from me. I also made sure to add that the AI shouldn't reply without my input.

I also opened ChatGPT’s notification settings and made sure task alerts could reach me outside the inbox. Depending on the account and notification category, ChatGPT can offer push, email, or text message delivery. I avoided email for obvious reasons, since an email announcing that ChatGPT had found an email would have possibly confused the AI.

With the task running, I shut Gmail and returned to everything else I had planned for the afternoon. For the first several minutes, I still felt the pull to check, but eventually, I stopped thinking about it.

The absence of constant email checks made the afternoon feel longer in a good way. I could finish a task without repeatedly glancing at another tab to make sure the internet had not developed an emergency specifically for me.

Only 3 interruptions

The first ChatGPT alert concerned a message from my bank about two hours after setting up the alert. ChatGPT identified the sender, explained why the email appeared to require attention, and told me what I needed to review. I opened that message directly, made sure it didn't actually require a response, and closed down the inbox.

The second alert came from a family member. This was a more interesting test because family messages rarely contain the tidy language that makes conventional email rules useful. They can require a decision or a quick response without the more business-style setup and keywords in professional messages.

Still, ChatGPT recognized that the message expected something from me and brought it to my attention and wrote a brief reply draft for me to approve. I added a little, but ChatGPT nailed the main points I needed to make, and it wasn't the kind of email that needed a lot of personal detail, so a few minutes after the alert arrived, the response was out the door.

The last alert concerned tickets I had recently purchased and a change to the itinerary for an upcoming event. In this case, the alert was very necessary as it changed some of the other plans for that day, and I had to let others know of the shift. That wasn't in ChatGPT's suggested response, of course, but just getting the heads up was useful.

Those were the only three times I looked at Gmail all afternoon. Other messages arrived, but ChatGPT did not summon me to look at them. When I went to my inbox afterward, I clocked only two emails that I might have wanted to deal with earlier, and neither had to be handled immediately. Nothing terrible happened because I didn't see a promotional email at the moment of delivery.

Anyone trying this should begin with a short test period and review the first few runs in ChatGPT’s Scheduled section. If ordinary receipts keep slipping through, add a clearer exclusion. If requests from real people are being missed, explain what counts as an expected response and allow ChatGPT to flag uncertain cases.

I would also keep the first version informational. Ask ChatGPT to summarize the obligation and point you toward the email, then make the decision yourself. It's a feature worth trialing. Many AI tools try to prove their value by producing more text, more summaries, and more notifications. This one worked because it knew when to leave me alone.

Skynet has missed another appointment. Depending on which Terminator timeline you prefer, the murderous artificial intelligence should have become self-aware in 1997, 2004, 2011, 2017, or perhaps last Tuesday. Each date has passed without a nuclear exchange. The machines have mostly occupied themselves by flattering users, mangling fingers in photos, and confidently inventing facts for gullible people to believe.

That has not stopped Silicon Valley from treating artificial general intelligence as both an imminent miracle and an approaching monster. AGI supposedly marks the moment a machine can match human intelligence across a broad range of tasks, though nobody can agree on which tasks, which humans, or what counts as intelligence.

Nvidia CEO Jensen Huang thinks it already happened and nobody noticed. “For many tasks, we could say that we’ve already achieved AGI,” he said during Nvidia’s latest earnings call. He added that such milestones are “kind of senseless at this point.” So much for Skynet.

But he is right that AGI has become close to useless as a measure of progress. Companies can redefine it according to whatever their newest model happens to do well. It's a promotion, not a scientific standard.

Apocalyptic decoy

But AI isn't harmless even if it's not inclined to play with nuclear bombs. There are less theatrical dangers from AI fueled by old-fashioned human greed and malevolence.

Today’s AI can produce endless fodder for scams. It can clone voices and faces well enough to trick loved ones out of money, use phishing messages to hack secure servers, and flood social networks with synthetic accounts that never tire of arguing. None of this qualifies as AGI under any serious definition, but the distinction offers little comfort to the person hearing a fake recording of a relative begging for money.

The fixation on AGI may actually make life harder for regulators.

Profit isn't a good yardstick

Huang argued that the concept of AGI should be replaced. We should consider if it is “doing productive and useful work” and “generating profitable tokens.” That makes sense for Nvidia, but to ordinary people worried about AI, profitability is not much of a safety test.

Scams are profitable. That's why there are so many. Firing human employees for an AI model that is 30% as good at 10% the price is profitable (in the short term). Neither result is something to brag about when describing the result of your product.

Retiring AGI as the organizing obsession and fear of the industry makes sense, but that doesn't mean AI companies can relax. Intelligence is too complex to serve as the sole trigger for concern. Power might be a better lens. We can measure who uses AI, and how, as well as who is expected to clean up when they fail. Then we'll know how explosive an impact it's had on our lives.

Conversations about liability, identity verification, ownership, and other legal details don't have the visual excitement of mushroom clouds, but regulators should make AI companies discuss them instead. We need to keep an eye on the human hands on the keyboard, not the perpetually delayed Skynet.

The numbers circulating through fintech right now are hard to ignore. Almost 10,000 jobs have been cut across the global sector in 2026 so far. PayPal is eliminating roughly 20% of its workforce. Block has shrunk to fewer than 6,000 employees, and Coinbase cut 14% of its headcount.

And hovering over all of it is a single explanation, repeated so often that it starts to remind us of a dot-com bubble — artificial intelligence. But is that actually what's happening? And more importantly, does it matter if the story isn't entirely true?

When the story becomes the strategy

AI accounted for 40% of all announced layoffs in May 2026, up from much lower levels earlier in the year. At first glance, it seems like a natural explanation: if the technology handles the routine work better than a human, then why not cut costs?

But the thing is, when we start looking closer at why exactly these mass layoffs are taking place, a deeper structural problem appears. So far, AI is just a good justification for how the industry communicates these shifts.

Deutsche Bank analysts identified this neat disguise in January. They called it "AI redundancy washing." Put simply, it’s the practice of attributing workforce reductions to AI adoption when the underlying drivers are far more familiar.

But if it’s not really AI which replaces the workers, then what are the real reasons for all these layoffs? If each company were to be considered individually, it could be anything, from deteriorating unit economics to the pressure of demonstrating operational discipline after years of “growth-at-all-costs.”

Research points to the same explanation: nearly 60% of US hiring managers acknowledged that they emphasize AI's role in layoffs just because it lands better with stakeholders. Indeed, a headcount reduction could have sparked fears about companies' growth models, but framing it as a strategic AI implementation makes it sound good.

The productivity paradox

Let’s assume that some of the companies are indeed replacing their workers with AI tools. In this case, there should be numbers to show it. But they don't share any effectiveness ratios, at least not yet.

A survey of 350 executives at companies with over $1 billion in revenue found that 80% of organizations piloting AI or autonomous technology reported workforce reductions. But after that, many honestly admit that they cut jobs regardless of whether the technology was actually generating returns.

This way, planning to spend another $10 million or more in 2026, companies are betting on a future that hasn't fully arrived. And trimming headcount in anticipation of a productivity windfall that remains, for most, theoretical.

Even if AI is implemented in a particular organization, it’s mostly in pilot mode, sitting on top of existing infrastructure rather than remaking it. Moreover, the AI payback picture is modest: only 25% of AI initiatives deliver the expected returns, and only 16% reach the scale of the entire organization.

McKinsey’s 2025 global AI survey found the same results. While most firms are using AI in at least one function, nearly 70% are still stuck in experimentation or pilot stages rather than scaling AI across the enterprise. And what’s more upsetting is that no more than 39% reported a significant EBIT impact from the technology adoption.

Where the capital is actually going

If AI isn't yet delivering the productivity revolution that many executives promised, then where is the industry's attention? The answer is less exciting than the headlines suggest — only last year, infrastructure and internal business systems received more than $200 billion worth of investments.

For now, fintech companies are under pressure to prove that all the promises made around AI weren't just another cycle of hype. So instead of reinventing financial services overnight, many are turning inward, looking for ways to make existing operations cheaper and easier to scale.

Even though the AI hasn’t brought the desired effect yet, the shift to automation is still important. For the past decade, fintech has largely been focused on the customer experience to offer a disruptive product, but from now on, more effort will go into the mechanisms behind the product than the product itself.

But this doesn’t mean that the "AI replaced the workers" narrative feels complete. Cutting headcount and replacing employees with bots may look good on a quarterly report, but it's not much of a strategy. Cost reduction can improve margins in the short term, but it rarely creates lasting differentiation.

So, in this environment, the winning ones will be those asking and answering honestly to a different question: What can we offer the market today that we couldn't offer a year ago?

The value of AI innovation beyond cost-cutting

Arguably, the more important part of AI adoption isn’t about how it replaces workers at all, but rather about what new value it creates. Historically, the biggest technological shifts rarely created value by simply making existing processes cheaper. They created entirely new products, services, and business models that were impossible before.

Without going too far in search of examples, let’s just remember the Internet. When it first arrived, a lot of people justified investing in it by bringing up “efficiency” aspects, such as digitizing paperwork or reducing communications costs through e-mails.

In reality, though, the Internet went much further than that, creating entirely new business models: online media, social networks, search engines, cloud software, and more. Giants like Google or Amazon did not get to where they are now simply because they optimized their processes. They found success precisely because they built products that simply couldn't have found success before the Internet became widely adopted.

Now, it’s AI’s turn to go down that road. What innovations it will bring is a story that still remains largely unexplored in fintech, but the potential is massive. In this sense, today’s hyper-focus on workforce cuts is likely going to be just a short phase in a much larger legacy that AI will leave in finance. The true winning move for companies now is to work out how they can use it to better serve their customers in new, never-before-explored ways.

Cost savings may be a measurable and immediate metric, but over time, the true advantage of adopting this technology will come from what it brings to the table, not what it takes away from it.

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Artificial intelligence may dominate today's headlines, but the most productive conversations I'm having across the technology ecosystem rarely begin or end with AI.

More often than not, they start with a business challenge.

Business challenges are driving the AI conversation

How do we modernize our infrastructure without increasing complexity? How do we strengthen our security posture while enabling innovation? How do we prepare our organization for AI without rebuilding everything from the ground up?

Those questions quickly lead to a realization: today's technology decisions are interconnected.

An AI initiative depends on cloud readiness. Cloud strategy influences cybersecurity. Identity, governance and data management underpin all of it. On paper, they look like separate line items. In practice, they're one system. If you pull on cloud, security moves with it; if you pull on AI, data and governance move too.

That's a significant shift, not only for organizations investing in technology, but for the partners helping them navigate it. Organizations are moving beyond asking whether AI matters. They're focused on how to implement it successfully.

That shift is happening across the industry.

Our fourth annual Direction of Technology Report, which surveyed more than 1,400 IT solution providers across 40 countries, found that nearly 75% of partners say AI is essential to their future. Yet the same research shows the conversation has become much broader than AI itself. Partners increasingly see services, specialization, cybersecurity and automation as the capabilities that will differentiate them in the years ahead.

To me, that's one of the most important signals in today's market, because the conversation isn't simply about adopting the next technology. It's about building an environment where technologies work together to solve real business challenges.

From experimentation to implementation

AI adoption is accelerating, but organizations quickly realize that successful AI initiatives depend on much more than selecting the right model or platform. They depend on secure infrastructure, trusted data, governance, integration and the expertise to bring those pieces together.

The market data tells a similar story.

Global AI spending is projected to exceed $2 trillion in 2026. Global Public Cloud spending is expected to surpass $1 trillion, while cybersecurity spending is projected to continue its rapid growth. These markets aren't growing in isolation but expanding together because organizations recognize that one investment reinforces the others.

That's why I believe the conversation is maturing.

The question is no longer, "Where do we start with AI?"

It's, "How do we build the right foundation to make AI successful?"

That requires a different approach from everyone in the technology ecosystem.

Customers aren't asking for another AI demonstration, but they’re asking questions like:

• How does AI fit into the technology investments we've already made?
• How will it integrate with our existing cloud environment?
• What does it mean for our security posture and governance?
• How can it improve productivity without creating unnecessary complexity?

Those are implementation questions, and I believe they'll define the next phase of AI adoption.

Technology will keep evolving, and AI will evolve fastest of all. What gives me confidence is not the pace of that innovation but the way the industry is organizing itself around the harder work of implementation.

No single vendor, provider or advisor holds every answer. Environments have become too interconnected for that, and the organizations making genuine progress are the ones drawing on combined expertise, weighing infrastructure, security, data and services together rather than in sequence.

The definition of success is changing with it. What matters is no longer how many technologies an organization can reach, but how well those technologies operate as one system in service of a business outcome. That, far more than any single breakthrough, will determine who ultimately gets value from AI tools.

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ChatGPT’s growing collection of plugins has always sounded more useful than it sometimes felt. A long directory can make every integration look equally promising, even though some become regular tools and others enjoy one exciting demonstration before joining the digital attic. I have installed plenty that seemed clever for approximately seven minutes.

A recent recommendation update is designed to make that search more useful. Plugin rankings now give greater priority to tools that people continue using after installation, helping relevant options appear more prominently across ChatGPT on the web and mobile.

That sounded like a useful reason to revisit the directory and see which plugins had earned a place in my own routine. Here are five ChatGPT plugins I use that you might find worthwhile integrating into your life too.

1. Spotify for soundtracks of my day

Music recommendations have traditionally fallen into two camps. An algorithm serves more of whatever you played last week, or a friend gives you a playlist that contains six hours of obscure ambient jazz and demands your gratitude. Spotify’s ChatGPT plugin offers a more conversational middle ground.

Once connected, it can use your listening history, favorite artists and habits to suggest music, find podcasts and build playlists. The advantage is the amount of context you can include. Instead of searching for “workout music,” you can request a precise length, mood, genre mixture and energy curve. You can then refine the result without starting again.

I find it particularly good for playlists attached to a specific activity. Give it constraints and a little personality. A useful prompt would be: “Spotify, make a 75-minute Saturday kitchen-cleaning playlist. Start with indie rock, increase the energy after 20 minutes, avoid songs I played heavily last month and finish with three ridiculous singalongs.”

That produces something more purposeful than a generic cleaning playlist assembled by somebody who apparently dusts to anonymous dance remixes. You can ask Spotify to replace individual tracks, make the selection calmer or rename the finished playlist before opening it in the Spotify app.

The plugin is also handy for rediscovery. Asking for artists you listened to frequently five or ten years ago can produce a surprisingly effective musical excavation, complete with the occasional song you had wisely buried.

2. AllTrails for touching grass

I like hiking and finding new places to go. The AllTrails plugin can turn my fuzzy desire to spend time outside into a shortlist that reflects how much time, energy,, and enthusiasm I actually have.

The trick is to describe the practical limits that generic searches tend to ignore. Include your location, maximum driving time, preferred distance, acceptable elevation gain, and any requirements involving children, dogs, shade, accessibility, or parking. ChatGPT can handle those details conversationally and use AllTrails to narrow the possibilities.

For example: “AllTrails, find three loop hikes within 45 minutes of me. Keep them between two and four miles with less than 700 feet of elevation gain. Prioritize recent positive reviews and parking that does not require arriving at dawn.”

That is far more useful than being told about the “10 best hikes in New York.” Follow-up prompts can compare the routes, suggest the best choice for a hot day, or identify the option with the gentlest opening mile. Trail conditions can change, so I still check recent reviews, closures, weather, and the official route before leaving, but it's been a great trip planner, especially for group hikes.

3. Todoist for due dates

My relationship with to-do lists has always been hopeful but unstable. I enjoy creating them, feel briefly organized, and then discover the list several days later beneath newer lists. Todoist already offered better structure, but its ChatGPT plugin removed much of the fiddly work involved in turning a collection of thoughts into an actual plan.

After connecting my Todoist account, I might describe a typical overloaded Saturday and ask it to put the errands in a sensible driving order and with a realistic time schedule for getting it all done. I also ask for a preview first lest ChatGPT overload me out of misplaced confidence in my abilities.

The plugin is especially useful for recurring household tasks. I can ask it to create reminders for changing filters, checking smoke alarms, renewing documents or ordering supplies.

4. FitAI Pro for fitness

FitAI Pro is designed for planning gym workouts and logging sets, It handles the organizational work that often stands between me and an actual workout.

After installing the plugin, I gave it my experience level, available equipment, preferred number of training days, and the maximum time I wanted to spend in the gym, and I get back a set of workouts to match. It helps to mention any movements you dislike or need to avoid

The large exercise library also makes substitutions easy for when I just don't like a particular exercise or want to mix things up. The plugin cannot perform the workout for me, which remains disappointing, but otherwise it streamlines workout plans in ways that make it harder for me to procrastinate or skip them entirely.

5. Podcast App to learn and laugh

Podcast discovery is even tougher than for plugins because it's such a vast sea of OK-to-bad options, with unfortunately rare gems hidden within. Even more so if you only want to find a particular episode and not just a whole show.

The Podcast App plugin lets me search by topic, idea, or program and receive relevant podcast recommendations. I use it for individual episodes, since that is where it saves the most time. You can mention a maximum length if you want to avoid the four-hour monologues, and specify just how much expertise you have if you want general knowledge versus granular detail beyond your interest. Those distinctions matter when the same subject can produce either an entertaining commute or a three-hour academic panel.

I also usually ask the plugin to explain in one sentence why each episode matches my request. I can find one episode about a specific book, scientific question, or historical incident and move on. My queue remains imperfect, but I don't spend more time looking than listening.

The updated plugin recommendation system makes good plugins easier to discover, but discovery is only the opening act. The real value comes from giving a plugin a well-defined job, useful constraints, and permission to ask for clarification. Specific prompts produce specific help. Vague prompts produce the digital equivalent of a sympathetic nod.

Still, I keep these five because they help in ways standard ChatGPT can't, or that would take much longer to set up in the general chatbox. As OpenAI acknowledges with the new system, returning voluntarily to them is the most convincing recommendation for any AI tool.

Now is the time for nonprofits to embrace artificial intelligence (AI).

There is a distinct opportunity to significantly improve their operations using the latest developments in AI and automation.

We are seeing confidence grow in the third sector as organizations come to understand the potential of AI, but now we are entering a crucial phase as they look to implement it.

Given the complex technology involved, it may appear daunting to get the right strategy in place, but a disciplined approach to adoption that is focused on incremental gains - not radical transformation - will ensure a positive outcome.

This will mitigate significant disruption by avoiding large number of components being upgraded at the same time, as that increases the risk of something going wrong.

A pragmatic approach combined with thinking big

Getting to this point is perhaps easier said than done, as many nonprofits rely on purpose-built applications and manual processes. It is incumbent on vendors to articulate a pragmatic approach to integrating AI with these existing systems, focusing on the idea of incremental improvements to processes and ways of working.

In parallel, nonprofits must think big about the potential for positive change. Vendors must work with their customers to help them understand that transformation entails more than just a chatbot or another point solution being added to the technology stack.

Done right, AI will reshape how workflows are triggered, data is interpreted and insights are generated. It will become an intelligence layer acting autonomously to interrogate data and provide teams with cognitive support to make more effective and real-time decisions.

Therefore, if nonprofits are to maximize their AI investments, they must ask themselves big questions: how does AI change the ecosystem the organization operates in? And how can it be used to improve the nonprofit’s mission?

If nonprofit leaders can think ambitiously about these answers, it will empower teams to become true knowledge workers. AI will help them uncover information to inform actions or give workers more time to focus on problem-solving, as the technology is designed to automate repetitive tasks that traditionally consume cognitive bandwidth.

The right data strategy is crucial

A key challenge for nonprofits is data governance, as the AI tools will only be effective if they have access to the right data. In a recent study we conducted called “Closing the Gap: Nonprofit Finance Software’s Crucial Role in Today’s Landscape”, 61% of finance professionals in US nonprofits admitted they still rely on generic spreadsheets for core financial management.

The combination of specialist applications and manual processes makes it difficult to connect information across different applications and workflows. Data must be extracted, transformed and loaded so that it can be analyzed. The extraction process is also challenging because the data is often not in the same format and must be loaded in a particular format for ease of use by the finance team.

To address the data challenge, nonprofits must decide how to make the right data accessible to the AI. The data must be set up in a way that can be interrogated in plain language, and it must be based on good data inputs. Aside from reliability, the data also requires a common analysis framework so that users can understand its origins and who is accountable for it. Then, the organizations must be able to analyze the data chain and how AI is interacting with the data to make decisions.

This requires a detailed appreciation of the semantics of data. For example, the term “project” means something different in the world of nonprofits compared a professional services company or a public sector organizations . The AI must be able to interpret the word correctly to avoid hallucinations.

Equally important is the ability to monitor, evaluate and adjust data as it goes through the data supply chain to avoid inaccuracies in areas such as when it moves from the record to report phase. Nonprofits must work closely with their technology suppliers to ensure everyone in the organization, whether at the leadership, program or project level, can monitor the data for compliance with expectations.

Incrementalism is better than quantum leaps

The concept of incrementalism, rather than the “Big Bang” or quantum leap approach to transformation is the best way to address these fundamental questions.

Most successful IT implementations follow a similar incremental path. For organizations embracing this approach and wanting to ensure operational integrity while transforming IT systems, it is important to start with an evaluation of where the organization is relative to the desired end state. The planning objective is to get to the end state through a process of continuous, incremental adoption of new functionality and applications.

Most importantly, this approach allows staff to build their confidence in collaborating with AI tools. Imagine a team operating in a remote location providing medical assistance and the head of the team realizes they need to order supplies. Using incremental innovation an organization could use a combination of AI and automation to alleviate the burden of replacing missing stock.

In the first phase, an AI tool could monitor existing stocks and prompt staff when it is running low. This will give employees the chance to train the AI tool when it is urgent to replenish stocks. The AI could also use contextual information such as historical usage data and information such as weather to identify likely peak demand. As the AI becomes more autonomous, it could make recommendations to a human co-worker suggesting it completes an order form for new supplies which is reviewed by an employee.

Again, this acts as a training opportunity for AI so that in time employees will have confidence it can autonomously complete such tasks in the background. In time, the whole process will minimize interruption for employees and reduce the burden on them to complete such administrative tasks.

What this example shows is that nonprofits do not have to achieve fully autonomous systems straight away. At each stage, the AI is providing workflow improvements that empower knowledge workers by saving them time to focus on what is important to deliver their missions.

Incremental change will see nonprofits delivering value to their organizations very quickly, if they are able to address fundamental questions around their approach to data, while also thinking big about what AI can enable them to do better.

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Paying $20 every month for ChatGPT Plus has always been easy to justify professionally. I use it for research, document analysis, and more than a little organizational assistance around the house.

Still, it's another monthly subscription at a time when everyone is looking for ways to reduce the amount repeatedly drained from their bank accounts. That's why I've been pressing ChatGPT to justify its own cost using the finance feature it debuted a couple of months ago.

I didn't ask ChatGPT to defend its place on my credit card bill literally, though it probably could. Instead, I prompted it to use its analysis of some of my finances to identify places where my money might be evaporating without enough justification, or even awareness.

ChatGPT’s finances tool can review connected accounts, including spending. Although it can't make any changes to how the money moves around, it can point accusingly at a subscription, leaving me to cancel it myself.

If you haven't done so, the setup is straightforward enough. Using ChatGPT Work on the web, you add the Finances plugin and connect whatever accounts you wish.

You can analyze recent spending right away, but I've found the weekly update from ChatGPT and nearly real-time updates to be particularly useful in my quest to make ChatGPT Plus cover its own cost.

Subscription cuts

Only a couple of weeks after starting to use the feature, ChatGPT raised in its weekly report that one of my streaming services seemed abnormally expensive. It turned out to be due to an add-on costing $11.99 a month.

I had subscribed to watch one particular series, finished it, and then apparently just left the subscription in place. ChatGPT found the repeated charge and placed it beside my other entertainment subscriptions.

A second recurring payment belonged to a music app costing $9.99 a month. I remembered signing up for a trial, but I could not remember using it after the first week. The service had quietly collected nearly $60 since.

Canceling those two subscriptions saved $21.98 a month. ChatGPT Plus had technically paid for itself during the first review, with $1.98 left over. That is not enough to retire on, but it is enough to make my article headline legally defensible.

ChatGPT also spotted that my internet bill had increased by $10 from one month to the next. The promotional rate had expired, but ChatGPT suggested I could still get the savings with the right language. The AI wrote out a few talking points to get me any current discounts. After a surprisingly tolerable phone call, the company applied a new promotion and knocked the $10 back off my monthly bill.

Saving on food

The subscription cuts were easy to measure — I only had to cancel them once and the savings appeared again the following month without any further actions necessary. Food spending was more complicated because there was no single charge I could remove and forget about, but ChatGPT helped me save money there too.

Ordinary takeout is more expensive than ever, but it's the delivery charges and service fees that really raise the final number. I'm not going to stop ordering takeout altogether and deprive myself of all the dishes I can't make myself. But switching to pickup as much as possible would make a big difference, according to ChatGPT. And it saved me about $35 over the next month.

ChatGPT also picked up several trips to convenience stores. Each transaction was small, usually a drink or some paper goods I'd forgotten to pick up at the grocery store. Nonetheless, convenience stores are more expensive than the grocery store, and the result added up quickly, as ChatGPT found in its analysis.

Just staying aware of that fact changed my habits and made me more likely to remember everything I needed to get at my regular shopping run and to bring my own drink when hitting the road.

After two months, the changes were saving me about $67 in a typical month. That figure included $21.98 from the two canceled subscriptions, $10 from the restored internet discount, and approximately $35 from spending less on delivery. Food costs will naturally move around, but the recurring savings alone were already covering the price of ChatGPT Plus.

I wouldn't blindly trust ChatGPT with finances or anything else. And I definitely wouldn't make it my first consultant for any big financial decisions. But these smaller changes are more important than you might think at first.

My Plus subscription still appears on the statement every month, but the reduced streaming and internet costs, not to mention fewer delivery fees, more than justify the $20 a month I'm paying for it.

My house contains a small but steadily expanding museum of obsolete technology. I've always been reluctant to get rid of them, even if I don't use them for years at a time, so they just fill up boxes in my attic. I know theoretically they're worth something, but I never feel motivated to actually go through them and see what people on eBay might pay.

But I thought ChatGPT might be able to do all of that for me from a simple photograph. I took a picture of the stuff in one of my boxes. Like many boxes stored in my house, this one had lost any obvious reason for existing. It contained a Nintendo Wii, two games, an elderly Kindle, and several DVDs that had survived multiple clear-outs.

I asked ChatGPT to identify everything, determine what might still have resale value, and check recent eBay sales to find out how much it might be worth. I specifically told ChatGPT to look at completed or sold listings, not merely the prices sellers were asking. Anyone can list a dusty iPod for $500 and describe it as rare; I wanted to know what a buyer actually paid.

Wiis and DVDs

ChatGPT had little trouble recognizing the white Nintendo Wii and the familiar Wii Sports sleeve. It also identified the second game as Marvel: Ultimate Alliance 2, although it asked for a closer photograph to confirm that the disc and manual were inside.

The DVDs were identified from their spines, but ChatGPT warned that ordinary used movies generally have very little individual resale value. It suggested checking for out-of-print titles, complete television series, unusual editions and sealed copies before assuming they belonged in a bulk lot.

The Kindle in the box turned out to be a first-generation Kindle Fire rather than one of Amazon’s E Ink readers. ChatGPT identified it from its thick black body and confirmed the model. It still worked, though the resale results were not especially exciting. Working first-generation Kindle Fires generally appear on eBay for about $10 to $20. Current eBay comparisons suggest a realistic selling price of about $15 to $20 for a tested, reset unit in decent condition.

A tidy sum and cleared space in my attic

The Wii powered up without complaint, which was more than I expected after its extended sabbatical. The disc drive worked, the remote connected and the console reached its home screen without producing any alarming noises. Recent white Wii sales put the market value of the console alone at approximately $40, with complete packages commanding more.

ChatGPT estimated the hardware at approximately $50 to $60. The more interesting discovery was Wii Sports. Since the game came with millions of consoles, I had assumed it was practically packaging material. Recent eBay transactions suggested otherwise. The current market is close to $30, almost as much as the system that plays it. People buying replacement consoles often want the game they remember playing with them, usually before someone put the remote through a television.

All told, ChatGPT said I might get between $115 and $135 for the complete box. The precise total would depend on condition, postage, selling fees, and whether buyers made lower offers. It would also require me to photograph everything properly, write accurate listings, and take seven packages to the post office. After a couple of days on the website, I managed to get pretty much what ChatGPT estimated, with $130 dollars for the lot.

The experiment worked because ChatGPT did more than name the objects. It asked for model numbers, checked whether accessories were present, distinguished complete games from loose discs, and noticed when an individual item deserved its own listing. It also prevented the DVDs from acquiring imaginary value merely because they had become old. It may not be the Antiques Roadshow retirement fund, but it's enough to encourage me to take more photos of the boxes in my attic this week.

ChatGPT has accumulated a surprisingly large collection of education tools, including Study Mode and new plugins designed specifically for college students and educators.

The trick is using them to learn rather than outsourcing the learning itself. OpenAI explicitly describes its approach as helping students understand material rather than simply shortcutting it, which is a useful rule to keep in mind before handing ChatGPT the first assignment of the semester.

Here are five ways I would set up ChatGPT before classes get properly underway.

1. Study Mode for all

ChatGPT's aptly named Study Mode is the obvious place to start with using the AI for learning, and it doesn't even require a paid subscription. You simply start a normal conversation with ChatGPT and type @study, or click the + button and search for Study.

Once activated, Study Mode changes ChatGPT's responses from direct answers to more Socratic behavior. It asks questions, provides hints, and checks whether you understand each step before moving forward.

Try prompts like, "Teach me derivatives, asking one question at a time, and give me a hint when I'm stuck."

You can also upload a photo of a problem or, where file uploads are available, add your class notes and ask ChatGPT to work from those. For instance, you could upload what you wrote down in class and tell ChatGPT to "Use these notes to teach me today's lesson. Start by identifying and explaining concepts I need to understand, then quiz me before moving to the next."

Study Mode can work with uploaded files and images, making it much more useful when it knows what your actual class is covering rather than delivering a generic miniature lecture.

2. ChatGPT College Plugin

A newer and more focused option is ChatGPT's College Student plugin. It's not universally available like Study Mode, only through ChatGPT Edu, so your university needs to offer it and make it available. Students who can access it get a collection of learning tools.

Of particular interest is its interactive learning site creation. You can share your course material to make interactive visual explanations, while also getting guided tutoring on concepts you find difficult. The AI can take uploaded lecture slides or notes and make a unique website full of study questions and able to provide feedback on your answers.

Depending on how confident you feel, you could ask ChatGPT for something as elaborate as, "Build me a study plan for the next two weeks using these course materials and my upcoming deadlines, and spend extra time on the topics I struggled with in the quiz."

3. Syllabus study

My memory of student life is that the syllabus traditionally enjoys about 20 minutes of intense attention before beginning its long journey toward the bottom of a backpack. ChatGPT can make it considerably more useful. Upload the syllabus to ChatGPT, and it can have a much more productive lifespan.

I'd suggest a prompt like, "Read this syllabus and create a week-by-week study plan. Include every test, paper, and deadline. Suggest what I should begin preparing for at least a week ahead of time."

You can ask about the extra busy weeks and ideas on making them as easy as possible too. If you let ChatGPT send you alerts, you might avoid the end-of-semester crunch when every deadline hits at once and you still only have 24 hours a day to study.

4. Flashcards

ChatGPT can also save you from spending an entire evening making flashcards instead of studying them. You can ask it to make flashcards from your notes, telling it to "Focus on particulalry confusing concepts and terms."

If you ask for one card at a time, it turns the exercise into an actual study session instead of generating another giant page of material to stare at, hopefully.

With the College Student plugin, flashcards can become part of the interactive learning experience rather than simply appearing as text in the conversation. You could ask: "Turn this week's lecture material into flashcards and organize them by topic."

5. Quiz time

Quizzing you on whether you actually have learned anything from studying is vital, and ChatGPT can be a big help in that regard. With its Study Mode, you can create practice questions, quiz yourself one question at a time, get it to explain mistakes and change the difficulty as you progress.

Upload your course material and try asking for a "10-question practice quiz based only on these notes. Ask one question at a time, don't reveal the answer until I respond, explain anything I get wrong, and make the next question harder when I answer correctly."

The College Student plugin can turn that same process into a more structured interactive quiz built around the course sources you provide. You can ask for more complex practice exams, and set up interactive tests that analyze your weakest areas for further study and quizzing.

With or without the plug-in, having ChatGPT close the metaphorical book and start asking questions is a much faster way to discover whether anything actually stuck.

• Apple Music will soon force artists to label their AI-generated tunes
• It follows moves from rival streaming services to limit AI music
• Some users want Apple to outright ban AI tracks from its platform

The subject of music generated by artificial intelligence (AI) has become a hot topic over the last couple of years, with the matter proving to be deeply polarizing among music lovers. Now, Apple is stepping in to enforce AI tags in Apple Music — but some users don’t think it’s going far enough.

According to The Hollywood Reporter, Apple sent out emails to “industry partners” earlier this week outlining its position. Those messages made it clear that AI labels will start appearing “later this year” and won't be optional.

“Content providers will be required to include AI Transparency Tags in any instance where AI was used to create a material portion of the content, including tracks that are AI platform generated,” Apple said. “We define AI-platform-generated content as anything that is primarily derived from a generative AI service.”

This comes a few months after Apple started allowing record labels and artists to voluntarily signal that tracks were made with AI assistance through so-called ‘Transparency Tags.’ The latest update goes further, though, requiring music distributors to flag AI content so consumers know exactly what they’re getting.

AI fraud and streaming manipulation

Apple didn’t outline how exactly it would enforce its new policy. It follows a similar move from Spotify, which revealed earlier this month that it would start labeling AI artists and would exclude them from curated and algorithmic recommendations. Rival service Tidal no longer pays uploaders if it detects that their submissions were made with AI.

Apple Music's new course of action comes amid growing disquiet over the use of AI in music creation. Popular concern over AI tracks has grown so much that tools like Detect Music have emerged to help people identify whether AI was used in the music they listen to.

Surveying social media, it’s clear that plenty of people support Apple’s move. “Good, but that took way too long,” lamented one person on Reddit. Another user wondered, “I might switch over to Apple Music once this is implemented, if there’s also an option to block AI content.”

Yet for others, Apple isn’t going far enough. “As a paying customer, you should be able to block it as a setting,” said one, while another contended that AI-generated music “shouldn’t be on the platform at all.” One Redditor was blunt, arguing that a “Better move would be to outright ban it.”

For many, the problem with AI music is a double one: feeling duped by the lack of artistry when a machine created the tracks you listen to, and concern that genuine musicians are being squeezed out as a result. Add to that the known cases of fraud and streaming manipulation that are often tied to the use of AI in music, and it’s clear that Apple felt that something had to be done.

With the proliferation of AI music-generation services like Suno and Beatoven, this is not a problem that will disappear overnight. But with AI tunes appearing to be highly unpopular — at least once they’re identified as such — Apple’s latest move could help to make a dent in their spread.

AI chatbots like ChatGPT, Claude, and Gemini are all keen to encourage you to use their service over their rivals, and often have ways to import your history and profile from one to another to some degree. That's great if you are planning a serious relocation, but sometimes you just want to take a single chat and bring it from one platform to another.

This can be especially useful if you're using the free version of Claude, for example, and you're about to reach your usage limit. By transferring the chat to another AI chatbot you can keep it going without waiting for your limits to refresh.

ThreadPort is a new browser extension for Google Chrome and Microsoft Edge designed to shorten that trip. It transfers a conversation currently open in ChatGPT, Claude, or Gemini to either of the other two services, carrying over the text of the discussion and some basic context setup as a single prompt plus transcript so the new AI chatbot can continue the conversation.

That makes ThreadPort fundamentally different from the built-in import tools offered by all three platforms. Instead of needing to learn everything about you and your conversational history. ThreadPort simply tells another AI what you have been discussing for the past few minutes.

You just need to install the extension in your browser and allow it access to ChatGPT, Claude, and Gemini websites. The access lets ThreadPort read the messages on one service and insert them into another, while browser storage is used for settings, temporary transfers, and the monthly usage count. The developer plans to make unlimited transfers a premium service with only 10 transfers a month for free, though at the moment the extension doesn't run out of transfers.

All you need to do then is have your chat on one of the platforms open, click on the extension, and pick another platform to send it to. The new platform will open automatically, and a transcript of the conversation, headed by an explanation from ThreadPort, will appear. You can set the extension to automatically submit the combined prompt, or leave it in draft form for your own editing first.

Vacation planning

To test ThreadPort, I opened ChatGPT and asked for help planning a three-day vacation. ChatGPT did its usual work of creating an itinerary with plenty of detail. I then clicked on ThreadPort and sent the conversation to Gemini.

A new Gemini tab opened with the entire conversation already formatted inside the prompt box, with my messages and ChatGPT’s responses clearly labeled. ThreadPort also stripped away interface debris such as buttons, citation controls, and widgets that would otherwise make a manual copy and paste messier.

Gemini received the trip plan exactly as I wanted, with a complete conversation rather than a crude summary. I then asked Gemini to assess the plan, without changing the underlying conversation before the transfer.

The process worked the same when sending to Claude from ChatGPT. ThreadPort supports all six directions among ChatGPT, Claude, and Gemini. But, as an additional test, I took the extended conversation from Gemini and asked ThreadPort to send it to Claude. The new Claude tab was basically the same, but it also mentioned in the opening prompt that it was a conversation "started on ChatGPT (via ChatGPT → Gemini)," and tagged which chatbot was responding in the transcript.

This is where ThreadPort might be at its best. Moving a live discussion preserves all the little decisions accumulated through follow-up questions. Those decisions often matter more than the initial prompt because they explain why the obvious suggestions are no longer useful.

It's also very fast, taking only seconds. Gemini's importer can take a lot longer as it requires an exported archive. Though it can absorb a much larger historical record, using it for a single vacation plan would be excessive.

Plus, Google’s tool is restricted to those with adult personal Google accounts, and you can't import your history if you're in the European Economic Area, Switzerland, or the United Kingdom. ThreadPort's creator, Juan Carlos Rampoldi, lives in Valencia, Spain, so a way around that restriction has obvious appeal.

Claude’s native option doesn't even allow for importing conversations. Claude can import memory, including preferences and personal details, but not actual history. But Claude and Gemini at least have some form of importing from rival AI chatbots. ChatGPT has a way of sending conversations between ChatGPT accounts, but nothing like the native importers for Gemini or Claude. ThreadPort at least opens up one possible avenue for doing so without having to craft and iterate on specialized prompts.

Rampoldi cited his own habit of moving among ChatGPT, Claude, and Gemini as the inspiration for ThreadPort after getting frustrated with manually copying conversations and losing formatting or context along the way. Notably, he built it with Claude Code, so it may have other flaws, but he made its source code publicly viewable on GitHub.

Smooth moves

I did appreciate how easy ThreadPort made comparing the models and their responses by making them all part of one larger conversation. Of course, the simplicity means there are limits. ThreadPort transfers text, but it cannot bring attached images or files. If I had uploaded hotel screenshots or a PDF itinerary into ChatGPT, I would have needed to upload those again manually in Claude and Gemini.

And if it was a very long conversation, ThreadPort might have needed to shorten it using what it calls "smart truncation," keeping the original task and the most recent exchanges while marking a section that might be removed.

The developer says ThreadPort has no servers and briefly holds the transcript in Chrome’s local extension storage before inserting it into the destination prompt box, after which it deletes the temporary copy.

That does not mean the conversation becomes private once you send it elsewhere. A ChatGPT transcript submitted to Claude is now being shared with Anthropic, and Gemini transcripts are still sent to Google.

Despite its limits, ThreadPort does come across as a very useful tool for those who like to employ multiple AI chatbots. Even as just a ferry for individual conversations, compared to a whole moving van importing your whole history, the extension is efficient and easy. It cuts down on a small but persistent bit of AI chatbot friction without trying to solve AI portability on a grand scale.

AI companies tend to build their products as though everyone will eventually choose one assistant and stay there. But if you want to be 'disloyal' and play the field, ThreadPort smooths the path nicely.

The most important aspect of the new ChatGPT for Teens feature will probably also annoy teenagers the most. If they ask it to do their homework for them, the chatbot is supposed to recognize it, and will instead steer the student toward figuring it out themselves rather than produce a ready-to-paste essay.

That sounds like a small adjustment to the usual ChatGPT formula, but it gets at one of the biggest problems with allowing students easy access to AI chatbots. While ChatGPT's abilities as a tutor, research assistant, and endlessly patient explainer are a potential boon to learning, letting it finish a student's homework while they scroll TikTok is something everyone should want to prevent.

ChatGPT for Teens is one way OpenAI is trying to steer away from that depressing scenario. The experience is intended for users aged 13 to 17 and is automatically applied when someone gives an age in that range or OpenAI's systems estimate that the user is under 18.

The Responsible Homework Reminder might actually bring some positives to the idea of teens using ChatGPT, not just ways of limiting harm. The feature supposedly recognizes when a teenager wants the AI to do their homework and redirects them toward Study Mode.

Questions instead of answers

There has always been a slightly absurd tension around AI and education. We want students to have access to technology that can explain almost anything on demand, but preferably without allowing that technology to do all the learning for them. Giving a teenager access to ChatGPT and assuming they'll always use it ethically and to help them learn is absurd. Even with the best intentions, the AI is usually slanted toward being overly helpful and undercutting the kind of education real research and effort provide.

OpenAI takes an engineering approach to a solution. ChatGPT for Teens combines Study Mode and its learning-centered conversations, quizzes, and now homework reminders. Teens and parents can also set Study Hours, making Study Mode the default during those times.

As Study Mode is built around guiding questions and mimicking educators, it should at least reduce the incidence of kids relying on ChatGPT to do the work for them. OpenAI says the system was developed with teachers and scientists to encourage active participation.

And while simply banning ChatGPT as much as possible sounds simpler, there's a reason some educators prefer solutions like ChatGPT for Teens. A teenager stuck on an algebra problem at 10:30 at night or who doesn't have the resources to hire a tutor can at least ask ChatGPT to help them work through the problem, and with Study Mode, that means more than just getting the answer.

“Not every student has someone at home who can help when they get stuck or have questions with their homework. With the right guardrails, AI can help fill that gap," Racquel Gibson, a high school math teacher in Miami, said in a statement. "Tools like Responsible Homework Reminder can guide students step by step, helping them understand their mistakes, build confidence, and keep learning — not simply hand them an answer. That kind of individualized support can make a meaningful difference for students who want to succeed but don’t always know how to move forward.”

There is an important caveat. Guardrails only matter if they actually hold up when a determined teenager starts rattling them. The age prediction operating behind the scenes should supposedly work out when a teen is using the AI. OpenAI says it can use signals including account age, usage patterns, topics discussed, and the times of day ChatGPT is used to estimate whether someone is under 18. OpenAI has admitted it's not a perfect system, but that it's still working to improve safeguards.

Teen safety

ChatGPT for Teens also includes reinforced guardrails to identify and prevent conversations around topics like self-harm and other violence, eating disorders, and sexual content. Those guardrails are arguably even more important than the ones stopping kids from using the AI to do their homework.

Even so, concerns about misinformation, chatbot addiction, and an overall degradation of thinking caused by AI chatbots are justifiably on the minds of many parents.

But at least the Responsible Homework Reminders are a gesture in the right direction, recognizing that the AI does not have to maximize how much work it performs on your behalf to be useful. For a teenager trying to learn, doing slightly less can occasionally be the more sophisticated feature.

If OpenAI can make that behavior reliable, teenagers may complain that ChatGPT has become less cooperative just when an assignment is due. But being less helpful might actually make ChatGPT much more useful in the long run.

ChatGPT has a new interactive quiz feature, designed to make testing yourself a more active part of using the chatbot.

Instead of asking ChatGPT to explain something and then nodding along because everything looks familiar, you can have it put you on the spot. It's part of OpenAI's steadily expanding learning toolkit, building on Study Mode and other features.

I picked five subjects where I felt reasonably confident, but it turned out to be a very efficient way to locate the holes in my knowledge.

How to use the quiz feature

The quiz feature is available to anyone. You just need to prompt ChatGPT by asking for it. I decided to start with a quiz about AI itself, specifically its history. But I didn't want to just have very basic questions, so I made sure to ask for the quiz to be at a certain level. And while you can ask for a short essay answer format, I opted for the classic multiple-choice style, and for help with where I went wrong.

My exact prompt asked ChatGPT to: “Quiz me on the history of AI at a college level with 10 multiple-choice questions. Don’t show me the answer until I respond. Explain anything I get wrong and give me my score at the end.”

The one-question-at-a-time format made a bigger difference than I expected. A conventional online quiz often gives you a page full of questions, followed by a score and perhaps a few explanations once you are finished. Here, ChatGPT reacted after every answer, briefly explained the result, and moved on.

I'll admit I was surprised at how difficult it became, but despite some errors, I did reasonably well, and actually learned some new facts.

I decided astronomy should be next. I have spent plenty of time reading about space, looking through telescopes, and collecting facts about stars, planets, and black holes, so I felt pretty safe. Unfortunately it felt like the AI took me telling it that ordinary solar system trivia was too easy after a few questions as some kind of personal insult.

“Make the remaining questions harder,” I told it, and ChatGPT took this to heart, with me facing questions on orbital mechanics and astronomical measurements that I barely got half right. Still, it's good to know that you can adjust the difficulty level on the fly.

That flexibility is something a fixed quiz cannot really offer. I could even tell ChatGPT exactly what was making things too easy, such as obvious wrong answers, and ask it to make all four choices plausible.

I decided I deserved something easier after that and chose 1990s pop culture. I grew up in the decade and figured it would be a breeze even if I asked for a high level of difficulty. I asked it to mix television, movies, music, video games, and technology questions, but to make it a little harder by "including questions that require more than just remembering titles. I managed a perfect score, despite not remembering exactly the composition of certain boy bands of the late 1990s and having to guess the right answer.

AI study buddy

I went for a more highbrow form of entertainment next with a quiz about Shakespeare, specifically asking ChatGPT to "avoid extremely famous quotes and obvious questions about plays like identifying the leads of Romeo and Juliet."

The questions about secondary characters and plot details made me glad it was multiple choice. If I were taking a real class on Shakespeare, I'd tell ChatGPT to note where I was failing and come back for further questions on it. I could even upload my notes to make sure I was being tested on what the class is covering.

My final category was world geography. At the suggestion of ChatGPT, I adjusted my prompt to make the quiz more dynamic in difficulty. I told the AI to "increase the difficulty of the questions every time I get three answers in a row correct."

If I was worried about getting cocky, that addition kept me very humble. The first six questions I breezed through, but enhancing the difficulty twice left me lost hunting for obscure mountains and the location of deep-sea trenches.

After I finished, I asked ChatGPT to analyze my answers, and the AI claimed it could distinguish between a silly mistake and a repeated gap in my knowledge. If I repeatedly struggled with a particular period, concept or kind of question, it offered to do a new round of questions focusing on that.

One crucial fact to remember is that ChatGPT can still get things wrong and hallucinate answers even in a quiz. If an answer seems off, you should check it independently. For more casual learning, the AI did quite well. You still have to do the studying yourself, but if you run out of practice questions, ChatGPT can help you keep studying ahead of any classroom quiz.

Artificial intelligence is entering a new phase, one defined not by experimentation, but by operational deployment in environments where the stakes are high and the margin for error is narrow.

Nowhere is this shift more visible than in critical services such as healthcare, where organizations are beginning to rely on AI not just for efficiency gains, but for decisions that directly affect lives, outcomes and public trust.

As a result, the conversation around AI capability is expanding, and there’s a real need for AI systems to be sovereign, trusted and aligned to the legal, ethical and operational frameworks of the jurisdictions they serve.

Sovereign AI is emerging as a response to this need.

It is not a marketing term or a technical preference; it is a structural requirement for organizations that operate under strict regulatory oversight and handle sensitive citizen data.

For these sectors, sovereignty is the mechanism that ensures AI systems remain under the control of the people and institutions accountable for their outcomes.

Data residency

The distinction between data residency and true sovereignty is central to this shift. Data residency simply describes where data is stored or processed. It is a geographical statement, not a legal one. Data sovereignty, by contrast, defines who controls the data, who can access it and which laws apply. It is a statement of legal authority and operational control.

Sovereign AI goes further still. A sovereign by design AI system ensures that every stage of the AI lifecycle, from training and fine tuning to inference, deployment and monitoring, sits entirely within the sovereign perimeter. This includes the IT infrastructure, the data pipelines, the model governance processes and the personnel who operate and maintain the system. Nothing crosses borders, and nothing falls under the jurisdiction of external authorities.

For critical services such as national healthcare systems, this level of assurance is not optional. These organizations must protect patient confidentiality, maintain public trust and comply with regulatory frameworks that are among the most stringent in the world. They cannot rely on AI systems whose training data is opaque, whose operational footprint spans multiple jurisdictions or whose governance structures are not aligned to local laws.

They need systems that are transparent, explainable and auditable, systems that can demonstrate not only what they do, but how and why they do it.

Regulated sectors

This is one of the reasons why organizations in regulated sectors are increasingly looking beyond general purpose AI models. These models have driven much of the recent excitement around AI, but they are not always suitable for environments where accuracy, safety and accountability are paramount.

Their training data is broad and often scraped from the open internet. Their provenance is difficult to verify. Their operational controls vary widely. And their governance frameworks are not always designed with regulatory compliance in mind. In contrast, domain specific AI models built on trusted, curated datasets offer a level of precision and contextual understanding that general purpose models struggle to match.

They can be aligned to clinical workflows, diagnostic pathways and sector specific terminology. They can be governed with the level of transparency and auditability that regulators increasingly expect. And when built within a sovereign architecture, they can operate entirely within the legal and ethical boundaries required by critical services.

The rise of sovereign AI signals a broader transformation in how regulated sectors will adopt and govern AI over the next decade. AI architectures will become more localized, with sovereign cloud regions, isolated compute environments and jurisdiction specific MLOps pipelines becoming the norm. Governance will become as important as model performance, with explainability, auditability and lifecycle control treated as first class requirements.

Regulators will demand greater transparency around model provenance, training data lineage and operational controls. And AI supply chains, from data ingestion to model deployment, will be scrutinized with the same rigor applied to other critical infrastructure.

What this future looks like

Healthcare offers a clear illustration of what this future looks like. When deployed responsibly, sovereign AI can automate clinical workflows while maintaining strict data protection, support diagnostic decision making with transparent and explainable models, improve patient flow through predictive analytics and optimize resource allocation across hospitals and care pathways.

By reducing administrative burden and helping ensure patients are directed to the most appropriate care pathway more efficiently, it also has the potential to improve productivity and support better use of constrained healthcare resources.

It can also enable population level insights without compromising privacy, allowing healthcare systems to plan more effectively and respond more rapidly to emerging challenges. These benefits are only achievable when the underlying AI systems are trusted, transparent and sovereign.

Sovereign AI represents a turning point in how critical services approach digital transformation. It acknowledges that trust, governance and domain expertise are just as important as model capability.

It recognizes that AI must be built to serve the needs, values and legal frameworks of the communities it supports. And it reflects a broader truth: as AI becomes more deeply embedded in essential services, sovereignty will not be a niche requirement. It will be the standard.

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Artificial intelligence is rapidly reshaping cybersecurity. Much of the conversation has focused on how large language models are helping developers write code, but a more significant shift may be occurring elsewhere: security testing.

AI systems are becoming remarkably effective at identifying vulnerabilities, forcing organizations to rethink how they evaluate the security of both software and hardware products.

The reason lies in the nature of modern software. Large codebases are sprawling ecosystems of interconnected modules, third-party dependencies, legacy components, and undocumented assumptions.

Security flaws often emerge not from a single defective function, but from subtle interactions between components that may be separated by hundreds of files or years of development history. Human reviewers excel at deep analysis, but they are constrained by time and cognitive bandwidth.

AI systems, by contrast, can rapidly traverse vast amounts of code, correlate information across repositories, and identify patterns associated with known vulnerability classes.

Common software weaknesses

This capability is particularly powerful for common software weaknesses such as memory-safety issues, race conditions, authentication flaws, insecure API usage, and privilege-escalation paths. In many cases, AI is acting as an amplifier for established security techniques rather than inventing new ones. Yet the result is still significant: vulnerabilities that previously required substantial manual effort to uncover can now be identified at a much greater scale and speed.

The implications for product security are profound. Organizations can no longer assume that obscure vulnerabilities will remain undiscovered because finding them is too expensive. The cost of vulnerability discovery is falling, and it is falling for defenders and attackers alike. Security testing therefore becomes less about achieving a point-in-time assessment and more about maintaining continuous assurance throughout the development lifecycle.

However, it would be a mistake to assume that all areas of cybersecurity will be transformed equally by AI. Hardware security, particularly side-channel analysis of cryptographic implementations, presents a very different challenge.

Unlike general-purpose software systems, cryptographic implementations operate within a comparatively narrow and mathematically defined problem space. Side-channel attacks do not typically exploit unexpected program behavior or complex interactions between software components. Instead, they target subtle information leakage through physical phenomena such as execution timing, power consumption, or electromagnetic emissions.

The challenge is not understanding millions of lines of source code; it is extracting meaningful signals from carefully collected measurements and applying sophisticated statistical techniques to reveal hidden secrets.

Side-channel analysis

This distinction matters because many of the strengths that make large language models effective in software security are less relevant in side-channel analysis. LLMs excel at connecting information across large bodies of text and code, identifying relationships that humans may overlook.

Side-channel research, by contrast, already relies heavily on structured datasets, statistical processing, signal analysis, and deep domain expertise. AI can certainly accelerate parts of the workflow—from automating experimentation to assisting with data interpretation—but the advantage is typically more incremental than transformational.

That does not make hardware security less important. If anything, it highlights the need for specialized testing approaches. As AI lowers the barriers to software vulnerability discovery, organizations may be tempted to assume that automated tools alone can provide comprehensive assurance.

The reality is that different classes of vulnerabilities require different forms of expertise. An AI-assisted code review may uncover a memory corruption bug, but it is unlikely to replace the specialist knowledge required to evaluate whether a cryptographic implementation leaks secrets through power analysis.

The broader lesson is that security testing is becoming more important, not less. AI is increasing the speed at which vulnerabilities can be found, but it is not eliminating the need for expert analysis. Instead, it is changing where that expertise delivers the most value.

Organizations that combine AI-assisted testing with rigorous human-led security evaluation will be best positioned to address the evolving threat landscape—whether the target is a cloud application, an embedded device, or the cryptographic hardware that underpins digital trust.

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